text:
\section{Introduction}
\label{intro-sec}

\begin{figure}[ht!b]
\begin{center}
\begin{scriptsize}
\begin{tabular}{|p{2.2in}|p{.2in}|p{.23in}|}
\hline
Quote {\bf Q}, Response {\bf R} & {\bf Sarc} & {\bf Nasty} \\ \hline
{\bf Q1}: I jsut voted. sorry if some people actually have, you know, LIVES and don't sit around all day on debate forums to cater to some atheists posts that he thiks they should drop everything for. emoticon-rolleyes emoticon-rolleyes emoticon-rolleyes As to the rest of your post, well, from your attitude I can tell you are not Christian in the least. Therefore I am content in knowing where people that spew garbage like this will end up in the End. &  & \\
{\bf R1}: No, let me guess . . . er . . . McDonalds. No, Disneyland. Am I getting closer? & 1 & -3.6
\\ \hline  \hline
{\bf Q2}: The key issue is that once children are born they are not physically dependent on a particular individual. &  &  \\
{\bf R2}  Really? Well, when I have a kid, I'll be sure to just leave it in the woods, since it can apparently care for itself. & 1 & -1 \\
\hline \hline
{\bf Q3}: okay, well i think that you are just finding reasons to go against Him. I think that you had some bad experiances when you were younger or a while ago that made you turn on God. You are looking for reasons, not very good ones i might add, to convince people.....either way, God loves you. :) &  &  \\
{\bf R3}: Here come the Christians, thinking they can know everything by guessing, and commiting the genetic fallacy left and right. & 0.8 & -3.4
\\ \hline
\end{tabular}
\end{scriptsize}
\end{center}
\caption{\label{sample-quote-response} Sample Quote/Response Pairs
  from {\small \tt 4forums.com} with Mechanical Turk annotations for
  Sarcasm and Nasty/Nice. Highly negative values of Nasty/Nice
  indicate strong nastiness and sarcasm is indicated
by values near 1.}
\vspace{-.3in}
\end{figure}

More and more of the information on the web is dialogic, from Facebook
newsfeeds, to forum conversations, to comment threads on news
articles.  In contrast to traditional, monologic Natural Language Processing
resources such as news,
highly social dialogue is very frequent in social media, as
illustrated in the snippets in Fig.~\ref{sample-quote-response} from
the publicly available Internet Argument Corpus ({\bf IAC})
\cite{Walkeretal12c}.  Utterances are frequently sarcastic, e.g., {\it
  Really?  Well, when I have a kid, I'll be sure to just leave it in
  the woods, since it can apparently care for itself} (R2 in
Fig.~\ref{sample-quote-response} as well as Q1 and R1), and are often
nasty, e.g.  {\it Here come the Christians, thinking they can know
  everything by guessing, and commiting the genetic fallacy left and
  right} (R3 in Fig.~\ref{sample-quote-response}).  Note also the
frequent use of dialogue specific discourse cues, e.g. the use of
{\it No} in R1, {\it Really? Well} in R2, and {\it okay, well} in Q3
in Fig.~\ref{sample-quote-response}~\cite{FoxTreeSchrock99,BryantFoxtree02,FoxTree10}.

The IAC comes with annotations of different types of social language
categories including sarcastic vs not sarcastic, nasty vs nice, rational
vs emotional and respectful vs insulting. Using a conservative
threshold of agreement amongst the annotators, an analysis of
10,003 Quote/Response pairs (Q/R pairs) from the {\small \tt 4forums}
portion of IAC suggests that social subjective language is fairly
frequent: about 12\% of posts are sarcastic, 23\% are emotional, and
12\% are insulting or nasty.  We select sarcastic and nasty dialogic turns
to test our method on more than one type of subjective language and explore
issues of generalization; we do not claim any relationship between
these types of social language in this work.

Despite their frequency, expanding this corpus of sarcastic or
nasty utterances at scale is expensive: human annotation of
100\% of the corpus would be needed to identify 12\% more
examples of sarcasm or nastiness. An explanation of how
utterances are annotated in IAC is detailed in Sec.~\ref{prev-sec}.

Our aim in this paper is to explore whether it is possible to extend a
method for bootstrapping a classifier for monologic, subjective sentences proposed by Riloff \& Wiebe, henceforth R\&W
\cite{RiloffWiebe03,ThelenRiloff02}, to automatically find
sarcastic and nasty utterances in unannotated online dialogues.
Sec.~\ref{method-sec} provides an overview of R\&W's
bootstrapping method. To apply bootstrapping, we:
\begin{enumerate}
\item Explore two different methods for identifying cue words and phrases in
two types of subjective language in dialogues: sarcasm and nasty  (Sec.~\ref{cue-sec});
\vspace{-.1in}
\item Use the learned indicators to train a sarcastic (nasty) dialogue act
  classifier that maximizes precision at the expense of recall (Sec.~\ref{hp-sec});
\vspace{-.1in}
\item Use the classified utterances to learn general
syntactic extraction patterns from the sarcastic (nasty) utterances (Sec.~\ref{pattern-sec});
\vspace{-.1in}
\item Bootstrap this process on unannotated text to learn
new extraction patterns to use for classification.
\vspace{-.1in}
\end{enumerate}

We show that the Extraction Pattern Learner improves
the precision of our sarcasm classifier by 17\% and the recall by
24\%, and improves the precision of the nastiness classifier
by 14\% and recall by 13\%.  We discuss previous work in
Sec.~\ref{prev-sec} and compare to ours in Sec.~\ref{discuss-sec}
where we also summarize our results and discuss future work.

\section{Previous Work}
\label{prev-sec}

IAC provides
labels for sarcasm and nastiness that  were
collected with Mechanical Turk on Q/R pairs such as those in
Fig.~\ref{sample-quote-response}. Seven Turkers per Q/R pair
answered a {\bf binary} annotation question for
sarcasm {\it Is the respondent using sarcasm?} (0,1) and a {\bf scalar} annotation
question for nastiness {\it Is the respondent attempting to be nice or
  is their attitude fairly nasty?} (-5 nasty $\ldots$ 5 nice).
We selected turns from IAC
Table~\ref{tab-nums} with sarcasm averages
above 0.5, and nasty averages below -1 and nice above
1.  Fig.~\ref{sample-quote-response} included example nastiness and
sarcasm values.

Previous work on the automatic identification of sarcasm has focused on Twitter using the
{\tt \#sarcasm} \cite{gonzalezetal11} and {\tt \#irony} \cite{Reyesetal12} tags and
a combined variety of tags and smileys \cite{davidovetal10}. Another popular
domain examines Amazon product reviews looking
for irony \cite{ReyesRosso11}, sarcasm \cite{tsuretal10}, and a corpus
collection for sarcasm \cite{Filatova12}. \cite{Carvalhoetal09} looks for
irony in comments in online newpapers which can have a thread-like
structure. This primary focus on monologic
venues suggests that sarcasm and irony can be detected with a relatively
high precision but have a different structure from dialogues
\cite{FoxTreeSchrock99,BryantFoxtree02,FoxTree10}, posing the question,
can we generalize from monologic to dialogic structures?
Each of these works use methods including LIWC unigrams, affect,
polarity, punctuation and more, and achieve on average a
precision of 75\% or accuracy of between 45\% and 85\%.

Automatically identifying offensive utterances is also of interest.
Previous work includes identifying flames in emails \cite{Spertus97}
and other messaging interfaces \cite{razavi2010offensive},
identifying insults in Twitter \cite{xiang2012detecting}, as well
as comments from new sites \cite{sood2011automatic}.
These approaches achieve an accuracy between 64\% and 83\%
using a variety of approaches. The accuracies for nasty utterances has
a much smaller spread and
higher average than sarcasm accuracies. This suggests that nasty
language may be easier to identify than sarcastic language.

\section{Method Overview}
\label{method-sec}

\begin{figure}[htb]
\centering
\includegraphics[width=3.25in]{bootstrap}
\caption{\label{bootstrap-fig} Bootstrapping Flow for Classifying Subjective Dialogue Acts, shown for sarcasm, but identical for nastiness.}
\end{figure}

Our method for bootstrapping a classifier for sarcastic (nasty)
dialogue acts uses R\&W's model adapted to our data as
illustrated for sarcasm in Fig.~\ref{bootstrap-fig}.
The overall idea of the method is to find reliable cues and
then generalize.
The top of Fig.~\ref{bootstrap-fig} specifies the input to the method
as an unannotated corpus
of opinion dialogues, to illustrate the long term aim of building a
large corpus of the phenomenon of interest without human annotation.
Although the bootstrapping method assumes that the input is {\bf
  unannotated text}, we first need utterances that are already labeled for
sarcasm (nastiness) to train it.
Table~\ref{tab-nums} specifies how
we break down into datasets the annotations on the utterances in
IAC for our various experiments.

\begin{table}[ht!]
\begin{small}
\begin{center}
\begin{tabular}{|p{0.75in}|c|c|c|}
\hline
SARCASM & \#sarc & \#notsarc & total    \\ \hline \hline
MT exp dev & 617  & NA & 617  \\ \hline
HP train  & 1407 & 1404 & 2811   \\ \hline
HP dev test   & 1614 & 1614 & 3228  \\ \hline
PE eval   & 1616 & 1616 & 3232  \\ \hline \hline
All &  5254 & 4635 & 9889   \\ \hline
\multicolumn{4}{c}{} \\
\multicolumn{4}{c}{} \\ \hline
NASTY &  \#nasty & \#nice & total   \\ \hline \hline
MT exp dev &  510 & NA & 510 \\ \hline
HP train  &  1147 & 1147 & 2294  \\ \hline
HP dev test &  691 & 691 & 1382 \\ \hline
PE eval &   691 & 691 & 1382 \\ \hline \hline
All   & 3039  & 2529 & 5568  \\ \hline
\end{tabular}
\caption{How utterances annotated for sarcasm (top) and nastiness (bottom) in IAC were used.
MT $=$ Mechanical Turk experimental development set. HP train $=$ utterances used to test whether combinations of cues could be used to develop a High
precision classifier. HP dev test $=$ ``Unannotated Text Collection'' in Fig.~\ref{bootstrap-fig}.
PE eval $=$ utterances used to train the Pattern Classifier.
\label{tab-nums}}
\end{center}
\end{small}
\end{table}

The left circle of Fig.~\ref{bootstrap-fig} reflects the assumption
that there are Sarcasm or Nasty Cues that can identify the
category of interest with high precision (R\&W call this the ``Known
Subjective Vocabulary''). The aim of first developing a high precision
classifier, at the expense of recall, is to select utterances that are reliably
of the category of interest from unannotated text. This is needed to
ensure that the generalization step of  ``Extraction Pattern Learner"  does
not introduce too much noise.

R\&W did not need to develop a ``Known
Subjective Vocabulary'' because previous work provided one
\cite{Wilsonetal05,Wiebeetal99,Wiebeetal03}. Thus, our first question
with applying R\&W's method to our data was whether or not it is
possible to develop a reliable set of Sarcasm (Nastiness) Cues ({\bf O1} below). Two factors
suggest that it might not be. First, R\&W's
method assumes that the cues are in the utterance to be classified,
but it has been claimed that sarcasm (1) is context dependent, and (2)
requires world knowledge to recognize, at least in many cases.
Second, sarcasm is exhibited by a wide range of different forms
and with different dialogue strategies such as jocularity,
understatement and hyberbole
\cite{Gibbs00,Eisterholdetal06,BryantFoxtree02,Filatova12}.  In
Sec.~\ref{cue-sec} we devise and test two different methods for
acquiring a set of Sarcasm (Nastiness) Cues on particular development sets
of dialogue turns called the ``MT exp dev" in Table~\ref{tab-nums}.

The boxes labeled ``High Precision Sarcastic Post Classifier'' and
``High Precision Not Sarcastic Post Classifier'' in
Fig.~\ref{bootstrap-fig} involves using the Sarcasm
(Nastiness) Cues in simple combinations that
maximize precision at the expense of recall. R\&W found
cue combinations that yielded a High Precision Classifier (HP Classifier)
with 90\% precision and 32\% recall on
their dataset. We discuss our test of these steps in
Sec.~\ref{hp-sec} on the ``HP train" development sets in
Table~\ref{tab-nums}
to estimate parameters for the High
Precision classifier, and then test the HP classifier with
these parameters on the test dataset labeled ``HP dev test" in
Table~\ref{tab-nums}.

R\&W's Pattern Based classifier increased recall to 40\%
while losing very little precision. The open question with applying
R\&W's method to our data, was whether the cues that we discovered, by
whatever method, would work at high enough precision to support
generalization ({\bf O2} below).  In
Sec.~\ref{pattern-sec} we describe how we use the ``PE eval" development set
(Table~\ref{tab-nums}) to estimate parameters for the Extraction
Pattern Learner, and then test the Pattern Based Sarcastic (Nasty) Post
classifier on the newly classified utterances
from the dataset labeled ``HP dev test" (Table~\ref{tab-nums}).
Our final open question was whether the extraction patterns from R\&W, which worked
well for news text, would work on social dialogue ({\bf O3} below). Thus
our experiments address the following open questions as to whether R\&W's
bootstrapping method improves classifiers
for  sarcasm and nastiness in online dialogues:
\begin{itemize}
\item ({\bf O1}) Can we develop a ``known sarcastic (nasty) vocabulary''? The LH
circle of Fig.~\ref{bootstrap-fig} illustrates that we use
two different methods to identify {\bf Sarcasm Cues}. Because we have utterances labeled as sarcastic, we compare a statistical method that extracts important features automatically from utterances, with a method that has a human in the loop, asking annotators to select phrases that are good indicators of sarcasm (nastiness)
(Sec.~\ref{hp-sec});
\vspace{-.1in}
\item ({\bf O2}) If we can develop a reliable set of sarcasm (nastiness) cues,
is it then possible to develop an HP classifier? Will our precision be high enough? Is the fact that sarcasm is often context dependent an issue?
(Sec.~\ref{hp-sec});
\vspace{-.1in}
\item ({\bf O3}) Will the extraction patterns used in R\&W's work
allow us to generalize sarcasm cues from the HP Classifiers?
Are R\&W's patterns
general enough to work well for dialogue and social language? (Sec.~\ref{pattern-sec}).
\end{itemize}

\section{Sarcasm and Nastiness Cues}
\label{cue-sec}

\begin{table}[t]
\begin{small}
\begin{center}
\begin{tabular}{|l|l|r|r|}
\hline
\multicolumn{4}{|c|}{\textbf{unigram}} \\
$\chi^2$ & MT & IA & FREQ \\ \hline \hline
right & ah & .95 & 2  \\ \hline
oh & relevant & .85 & 2 \\ \hline

we & amazing & .80 & 2 \\ \hline
same & haha & .75 & 2 \\ \hline
all  & yea & .73 & 3   \\ \hline
them & thanks & .68 & 6   \\ \hline
mean & oh & .56 & 56  \\ \hline \hline
\multicolumn{4}{|c|}{\textbf{bigram}} \\
 $\chi^2$ & MT   & IA & FREQ
\\ \hline \hline
the same & oh really & .83 & 2 \\ \hline
 mean like & oh yeah & .79 & 2 \\ \hline
trying to & so sure & .75 & 2 \\ \hline
that you & no way  & .72 & 3  \\ \hline
oh yeah & get real & .70 & 2  \\ \hline
I think & oh no & .66 & 4  \\ \hline
we should & you claim  & .65 & 2  \\ \hline \hline
\multicolumn{4}{|c|}{\textbf{trigram}}\\
 $\chi^2$ & MT   & IA & FREQ
\\ \hline \hline
 you mean to & I get it & .97 & 3  \\ \hline
mean to tell & I'm so sure & .65 & 2  \\ \hline
have to worry & then of course & .65 & 2  \\ \hline
sounds like a & are you saying  & .60 & 2  \\ \hline
to deal with & well if you  & .55 & 2  \\ \hline
I know I & go for it  & .52 & 2  \\ \hline
you mean to & oh, sorry &  .50 & 2  \\ \hline
\end{tabular}
\caption{Mechanical Turk (MT) and $\chi^2$ indicators for Sarcasm \label{fig:sarc_inds} }
\end{center}
\end{small}
\end{table}

\begin{table}[t]
\begin{small}
\begin{center}
\begin{tabular}{|l|l|r|r|}
\hline

\multicolumn{4}{|c|}{\textbf{unigram}} \\
$\chi^2$ & MT & IA & FREQ \\ \hline \hline
like & idiot & .90 & 3 \\ \hline
them & unfounded & .85 & 2 \\ \hline
too & babbling & .80 & 2 \\ \hline
oh & lie & .72 & 11  \\ \hline
mean & selfish & .70 & 2 \\ \hline
just & nonsense & .69 & 9 \\ \hline
make & hurt & .67 & 3 \\ \hline \hline
\multicolumn{4}{|c|}{\textbf{bigram}} \\
 $\chi^2$ & MT   & IA & FREQ
\\ \hline \hline
 of the & don't expect & .95 & 2 \\ \hline
you mean & get your & .90 & 2 \\ \hline
yes, &   you're an & .85 & 2 \\ \hline
 oh, & what's your & .77 & 4 \\ \hline
 you are & prove it &  .77 & 3 \\ \hline
 like a & get real & .75 & 2 \\ \hline
 I think & what else  & .70 & 2 \\ \hline  \hline
\multicolumn{4}{|c|}{\textbf{trigram}}\\
 $\chi^2$ & MT   & IA & FREQ
\\ \hline \hline
to tell me & get your sick   & .75 & 2  \\ \hline
would deny a & your ignorance is  &.70 & 2 \\ \hline
like that?  & make up your  & .70 & 2 \\ \hline
mean to tell & do you really  & .70 & 2 \\ \hline
sounds like a & do you actually &  .65 & 2 \\ \hline
you mean to & doesn't make it & .63 & 3 \\ \hline
to deal with & what's your point   & .60 & 2\\ \hline
\end{tabular}
\caption{Mechanical Turk (MT) and $\chi^2$ indicators for Nasty
\label{fig:nast_inds} }

\end{center}
\end{small}
\end{table}

Because there is no prior ``Known Sarcastic Vocabulary" we pilot
two different methods for discovering lexical cues to sarcasm and
nastiness, and experiment with combinations of cues that could yield
a high precision classifier \cite{Gianfortonietal11}. The first
method uses $\chi^2$ to measure whether a word or phrase is
statistically indicative of sarcasm (nastiness) in the development sets
labeled ``MT exp dev" (Table~\ref{tab-nums}).
This method, a priori, seems reasonable because it is likely that if
you have a large enough set of utterances labeled as sarcastic,
you could be able to automatically learn a set of reliable cues for sarcasm.

The second method introduces a step of human annotation.
We ask Turkers to identify
sarcastic (nasty) indicators in utterances (the open question {\bf O1}) from
the development set ``MT exp dev" (Table~\ref{tab-nums}).
Turkers were presented with utterances previously
labeled sarcastic or nasty in IAC by 7 different Turkers,
and were told ``In a previous study, these responses were identified
as being sarcastic by 3 out of 4 Turkers. For each quote/response pair,
we will ask you to identify sarcastic or potentially sarcastic phrases
in the response". The Turkers then selected words or phrases from the response
they believed could lead someone to believing
the utterance was sarcastic or nasty. These utterances were
not used again in further experiments. This crowdsourcing method is
similar to \cite{Filatova12}, but where their data is monologic,
ours is dialogic.

\subsection{Results from Indicator Cues}

\begin{figure}[h]
\centering
\includegraphics[width=0.45\textwidth]{ita_trigram40.png}
\vspace{-.2in}
\caption{Interannotator Agreement for sarcasm trigrams}
\label{fig:tri40} \end{figure}

Sarcasm is known to be highly variable in form, and to depend, in some
cases, on context for its interpretation
\cite{SW81,Gibbs00,BryantFoxtree02}. We conducted an initial
pilot on 100 of the 617 sarcastic utterances in the development set
``MT exp dev" to see
if this was necessarily the case in our dialogues. \cite{Snowetal08}
measures the quality of Mechanical Turk annotations on common
NLP tasks by comparing them to
a gold standard. Pearson's correlation coefficient shows that very few
Mechanical Turk annotators were required to beat the gold standard data,
often less than 5.
Because our sarcasm task does not have gold standard data, we ask
100 annotators to participate in the pilot. Fig.~\ref{fig:tri40} plots the average
interannotator agreement
(ITA) as a function of the number of annotators, computed using
Pearson correlation counts, for 40 annotators and for
trigrams which require more data to converge. In all cases
(unigrams, bigrams, trigrams) ITA plateaus at around 20 annotators
and is about 90\% with 10 annotators, showing that the Mechanical Turk
tasks are well formed and there is high agreement.
Thus we elicited only 10 annotations for the remainder of
the sarcastic and all the nasty utterances from the development
set ``MT exp dev".

We begin to form our ``known sarcastic vocabulary" from these
indicators, (open question {\bf O1}).
Each MT indicator has a {\bf FREQ} (frequency): the number
of times each indicator appears in the training set; and an {\bf IA}
(interannotator agreement):
how many annotators agreed that each indicator was sarcastic or nasty.
Table~\ref{fig:sarc_inds} shows the best unigrams, bigrams, and
trigrams from the $\chi^2$ test and from the sarcasm Mechanical Turk
experiment and Table~\ref{fig:nast_inds} shows the results from the nasty
experiment. We compare the MT
indicators to the $\chi^2$ indicators as part of investigating
open question {\bf O1}.

As a pure statistical method, $\chi^2$ can pick out
things humans might not. For example, if it just happened that the word
`we' only occurs in sarcastic utterances in the development set,
then $\chi^2$ will select it as a strong sarcastic word (row 3 of
Table~\ref{fig:sarc_inds}). However,
no human would recognize this word as corresponding to sarcasm.
$\chi^2$ could easily be overtrained
if the ``MT exp dev" development set is not large enough to eliminate
such general words from consideration, ``MT exp dev" only has 617
sarcastic utterances and 510 nasty utterances (Table~\ref{tab-nums}).

Words that the annotators select as indicators (columns labeled MT in
Table~\ref{fig:sarc_inds} and Table~\ref{fig:nast_inds})
are much more easily identifiable although they do not
appear as often. For example, the {\bf IA} of 0.95 for
'ah' in Table~\ref{fig:sarc_inds} means that of all the annotators who saw 'ah' in the utterance
they annotated, 95\% selected it to be sarcastic. However the {\bf FREQ}
of 2 means that `ah' only appeared in 2
utterances in the ``MT exp dev" development set.

We test
whether any of the methods for selecting indicators provide reliable
cues that generalize to a larger dataset in
Sec.~\ref{hp-sec}. The parameters that we estimate on the
development sets are exactly how frequent (compared to a $\theta_1$)
and how reliable (compared to a $\theta_2$) a cue
has to be to be useful in R\&W's bootstrapping method.

\section{High-Precision Classifiers}
\label{hp-sec}

R\&W use their ``known subjective vocabulary" to train a High
Precision classifier. R\&W's HP classifier searches for exact surface
matches of the subjective indicators and classifies utterances as
subjective if two subjective indicators are present.  We follow
similar guidelines to train HP Sarcasm and Nasty Classifiers. To test
open question {\bf O1}, we use a development set called
``HP train" (Table~\ref{tab-nums}) to test three methods for
measuring the ``goodness'' of an
indicator that could serve as a high precision cue: (1) interannotator
agreement based on annotators consensus from Mechanical Turk, on
the assumption that the number of annotators that select a cue
indicates its strength and reliability ({\it IA features}); (2)
percent sarcastic (nasty) and frequency statistics in the HP train
dataset as R\&W do ({\it percent features}); and (3) the $\chi^2$ percent
sarcastic (nasty) and frequency statistics ({\it $\chi^2$ features}).

The {\it IA features} use the MT indicators and the {\bf IA} and {\bf FREQ}
calculations introduced in Sec.~\ref{cue-sec}
(see Tables~\ref{fig:sarc_inds} and~\ref{fig:nast_inds}).
First, we select indicators
such that $\theta_1 <= $ {\bf FREQ} where $\theta_1$ is a set of possible thresholds.
Then we introduce two new parameters $\alpha$ and $\beta$
to divide the indicators into three ``goodness'' groups that reflect
interannotator agreement.
\begin{displaymath}
\begin{scriptsize}
   indicator strength = \left\{
  \begin{array}{l l}
    weak 		& \quad if \ 0 \le \text{{\bf IA}} < \alpha  \\
    medium	& \quad if \ \alpha \le \text{{\bf IA}} < \beta  \\
    strong		& \quad if \ \beta \le \text{{\bf IA}} < 1  \\
  \end{array} \right.
\end{scriptsize}
\end{displaymath}

For {\it IA features}, an utterance is classified as
sarcastic if it contains at least one {\it strong} or two {\it medium} indicators. Other conditions were piloted. We first
hypothesized that weak cues might be a way of classifying ``not
sarcastic'' utterances. But HP train showed that both sarcastic and
not sarcastic utterances contain weak indicators yielding no
information gain. The same is true for Nasty's counter-class Nice.  Thus
we specify that counter-class utterances must have no
{\it strong} indicators or at most one {\it medium} indicator.
In contrast,  R\&W's counter-class classifier
looks for a maximum of one subjective indicator.

The {\it percent features} also rely on the {\bf FREQ} of each MT
indicator, subject to a $\theta_1$ threshold, as well as the percentage of
the time they occur in a sarcastic utterance ({\bf \%SARC}) or nasty utterance
({\bf \%NASTY}). We select indicators with various parameters for
$\theta_1$ and $\theta_2 \le $ {\bf \%SARC}. At least two indicators
must be present and above the thresholds to be classified and we
exhaust all combinations. Less than two indicators are needed to be
classified as the counter-class, as in R\&W.

Finally, the {\it $\chi^2$ features} use the same method as {\it percent
features} only using the $\chi^2$ indicators instead of the MT indicators.

After determining which parameter settings performs the
best for each feature set, we ran the HP classifiers,
using each feature set and the best
parameters, on the test set labeled ``HP dev test".
The HP Classifiers classify the
utterances that it is confident on, and leave others unlabeled.

\subsection{Results from High Precision Classifiers}

The HP Sarcasm and Nasty Classifiers were trained on the
three feature sets with the following parameters: {\it IA features}
we exhaust all combinations of $\beta =$ [.70, .75, .80, .85, .90, .95, 1.00],
$\alpha =$ [.35, .40, .45, .50, .55, .60, .65, .7],  and $\theta_1$ $=$ [2, 4, 6, 8, 10];
for the {\it percent features} and {\it $\chi^2$ features} we again
exhaust $\theta_1$ $=$ [2, 4, 6, 8, 10] and $\theta_2$ $=$ [.55, .60,
  .65, .70, .75, .80, .85, .90, .95, 1.00].

Tables~\ref{sarcasm-train_results} and ~\ref{nasty-train_results} show
a subset of the experiments with each feature set. We want to select
parameters that maximize precision without sacrificing too much
recall.  Of course, the parameters that yield the highest precision
also have the lowest recall, e.g.  Sarcasm {\it percent features},
parameters $\theta_1 = 4$ and $\theta_2 = 0.75$ achieve 92\% precision
but the recall is 1\% (Table~\ref{sarcasm-train_results}), and Nasty
{\it percent features} with parameters $\theta_1 = 8$ and $\theta_2 =
0.8$ achieves 98\% precision but a recall of 3\%
(Table~\ref{nasty-train_results}).  On the other end of the spectrum,
the parameters that achieve the highest recall yield a precision
equivalent to random chance.

\begin{table}[ht!]
\begin{scriptsize}
\begin{center}
\begin{tabular}{|p{0.40in}|r|c|c|r|}
\hline
SARC &  PARAMS  & P & R & N (tp)
\\ \hline \hline
\% &  $\theta_1=$4, $\theta_2=$.55  & 62\% & 55\% &  768 \\ \hline
 &  4, .6  & 72\% & 32\% &  458 \\ \hline
 &  4, .65  & 84\% & 12\% &  170 \\ \hline
 &  4, .75  & 92\% & 1\% &  23 \\ \hline \hline

IA &  $\theta_1=$2, $\beta=$.90,  $\alpha=$.35  & 51\% & 73\% &   1,026 \\ \hline
 &  2, .95,  .55  & 62\% & 13\% &  189 \\ \hline
 &  2, .9, .55  & 54\% & 34\% &  472 \\ \hline
 &  4, .75, .5  & 64\% & 7\% &  102 \\ \hline
 &  4, .75, .6  & 78\% & 1\% &  22 \\ \hline \hline

$\chi^2$ &  $\theta_1=$8, $\theta_2=$.55  & 59\% & 64\% &  893 \\ \hline
 &  8, .6  & 67\% & 31\% &  434  \\ \hline
 &  8, .65  & 70\% & 12\% &  170 \\ \hline
 &  8, .75  & 93\% & 1\% &  14 \\ \hline
\end{tabular}
\caption{Sarcasm Train results; P: precision, R: recall, tp: true positive classifications
\label{sarcasm-train_results}}
\end{center}
\end{scriptsize}
\end{table}

\begin{table}[ht!]
\begin{scriptsize}
\begin{center}
\begin{tabular}{|p{0.40in}|r|c|c|r|}
\hline
NASTY &  PARAMS  & P & R & N (tp)
\\ \hline \hline
\% &  $\theta_1=$2, $\theta_2=$.55  & 65\% & 69\% & 798\\ \hline
 &  4, .65  & 80\% & 44\% & 509 \\ \hline
 &  8, .75  & 95\% & 11\% & 125 \\ \hline
 &  8, .8  & 98\% & 3\% & 45 \\ \hline \hline

IA &  $\theta_1=$2, $\beta=$.95, $\alpha=$.35  & 50\% & 96\% &  1,126 \\ \hline
 &  2, .95,  .45  & 60\% & 59\% & 693 \\ \hline
 &  4, .75,   .45  & 60\% & 50\% & 580 \\ \hline
 &  2, .7,   .55  & 73\% & 12\% & 149 \\ \hline
 &  2, .9,   .65  & 85\% & 1\% & 17 \\ \hline \hline

$\chi^2$ &   $\theta_1=$2, $\theta_2=$.55  & 73\% & 15\% & 187 \\ \hline
 &  2, .65  & 78\% & 8\% & 104 \\ \hline
 &  2, .7  & 86\% & 3\% & 32 \\ \hline
\end{tabular}
\caption{Nasty Train results; P: precision, R: recall, tp: true positive classifications
\label{nasty-train_results}}
\end{center}
\end{scriptsize}
\end{table}

Examining the parameter combinations
in Tables~\ref{sarcasm-train_results} and ~\ref{nasty-train_results}
shows that {\it percent features} do better than {\it IA features}
in all cases in terms of precision. Compare the block of results labeled \%
in Tables~~\ref{sarcasm-train_results} and \ref{nasty-train_results}
with the IA and $\chi^2$ blocks for column P.
Nasty appears to be easier to identify than Sarcasm, especially using the {\it percent features}.
The performance of the {\it $\chi^2$ features} is comparable to that of
{\it percent features} for sarcasm, but lower than {\it percent features}
for Nasty.

The best parameters selected from each feature set are shown in the
{\sc params} column of Table~\ref{hp_final_results}. With the
indicators learned from these parameters, we run the Classifiers on the test
set labeled ``HP
dev test" (Table~\ref{tab-nums}).  The performance on test set ``HP dev test"
(Table ~\ref{hp_final_results}) is worse than on the training set
(Tables ~\ref{sarcasm-train_results} and
~\ref{nasty-train_results}). However we conclude that {\bf both the
  \% and $\chi^2$ features} provide candidates for sarcasm (nastiness)
cues that are high enough precision (open question {\bf O2}) to be used in the
Extraction Pattern Learner (Sec.~\ref{pattern-sec}), even if Sarcasm
is more context dependent than Nastiness.

\begin{table}
\begin{scriptsize}
\begin{center}
\begin{tabular}{|p{0.45in}|r|c|c|c|c|}
\hline
& PARAMS & P & R & F    \\ \hline \hline
Sarc \% &  $\theta_1=$4, $\theta_2=$.55  & 54\% & 38\% & 0.46 \\ \hline
Sarc IA &  $\theta_1=$2,  $\beta=$.95,  $\alpha=$.55  & 56\% & 11\% & 0.34 \\ \hline
Sarc $\chi^2$ &  $\theta_1=$8, $\theta_2=$.60  & 60\% & 19\% & 0.40 \\ \hline \hline
Nasty \% &  $\theta_1=$2, $\theta_2=$.55  & 58\% & 49\% & 0.54 \\ \hline
Nasty IA &  $\theta_1=$2, $\beta=$.95, $\alpha=$.45  & 53\% & 35\% & 0.44 \\ \hline
Nasty $\chi^2$ &  $\theta_1=$2, $\theta_2=$.55  & 74\% & 14\% & 0.44 \\ \hline
\end{tabular}
\caption{HP Dev test results; {\sc PARAMS}: the best parameters for
each feature set P: precision, R: recall, F: f-measure}
\label{hp_final_results}
\end{center}
\end{scriptsize}
\end{table}

\section{Extraction Patterns}
\label{pattern-sec}

R\&W's Pattern Extractor searches for instances of the 13 templates in
the first column of Table~\ref{tab:templates} in utterances classified
by the HP Classifier. We reimplement this; an example of each
pattern as instantiated in test set ``HP dev test" for our data is shown in the
second column of Table~\ref{tab:templates}. The template {\tt \small
  <subj> active-verb <dobj>} matches utterances where a subject is
followed by an active verb and a direct object. However, these matches are not
limited to exact surface matches as the HP Classifiers required,
e.g. this pattern would match the phrase ``have a problem".
Table~\ref{extract-pattern-fig} in the Appendix provides example utterances
from IAC that match the instantiated template patterns.
For example, the excerpt from the first row in Table~\ref{extract-pattern-fig}
``It is quite strange to encounter someone in this day and age who lacks any knowledge
whatsoever of the mechanism of adaptation since it {\bf was explained} 150 years ago"
matches the {\tt \small <subj> passive-verb} pattern. It appears 2 times ({\bf FREQ})
in the test set and is sarcastic both times ({\bf \%SARC} is 100\%). Row 11 in
Table~\ref{extract-pattern-fig} shows an utterance matching the
{\tt \small active-verb prep <np>} pattern with the phrase
``At the time of the Constitution there weren't exactly vast suburbs that
could be prowled by thieves {\bf looking for} an open window". This phrase
appears 14 times ({\bf FREQ}) in the test set and is sarcastic ({\bf \%SARC}) 92\% of the
time it appears.

\begin{table}[ht!]
\begin{small}
\begin{center}
\begin{tabular}{|l|l|l|l|}
\hline
Synactic Form	&	Example Pattern	\\ \hlin
$<$subj$>$ passive-verb	&	$<$subj$>$ was explained \\
$<$subj$>$ active-verb	&	$<$subj$>$ appears	\\%&	1	&	0.94	\\
$<$subj$>$ active-verb dobj	&	$<$subj$>$ have problem	\\%&	4	&	0.5	\\
$<$subj$>$ verb infinitive	&	$<$subj$>$ have to do	\\%&	15	&	0.86	\\
$<$subj$>$ aux noun	&	$<$subj$>$ is nothing	\\%&	10	&	0.9	\\
active-verb $<$dobj$>$	&	gives $<$dobj$>$	\\%&	25	&	0.88	\\
infinitive $<$dobj$>$	&	to force $<$dobj$>$	\\%&	9	&	0.89	\\
verb infinitive $<$dobj$>$	&	want to take $<$dobj$>$	\\%&	5	&	0.8	\\
noun aux $<$dobj$>$	&	fact is $<$dobj$>$		\\%&	6	&	0.83	\\
noun prep $<$np$>$	&	argument against $<$np$>$\\
active-verb prep $<$np$>$	&	looking for $<$np$>$	\\%&	14	&	0.92	\\
passive-verb prep $<$np$>$	&	was put in $<$np$>$	\\%&	3	&	0.66	\\
infinitive prep $<$np$>$	&	to go to $<$np$>$		\\ \hlin
\end{tabular}
\caption{Syntactic Templates and Examples of Patterns that were Learned for Sarcasm. Table.~\ref{extract-pattern-fig} in the Appendix provides example posts that instantiate these patterns.}
\label{tab:templates}
\end{center}
\end{small}
\end{table}

The Pattern Based Classifiers are trained on a development set labeled ``PE eval"
(Table~\ref{tab-nums}). Utterances from this development set are not used
again in any further experiments. Patterns are extracted from the dataset and we
again compute {\bf FREQ} and {\bf \%SARC} and {\bf \%NASTY} for each
pattern subject to $\theta_1 \le$ {\bf FREQ} and $\theta_2 \le$ {\bf
  \%SARC} or {\bf \% NASTY}.  Classifications are made if at least two
patterns are present and both are above the specified $\theta_1$ and
$\theta_2$, as in R\&W. Also following  R\&W, we do not learn
``not sarcastic" or ``nice" patterns.

To test the Pattern Based Classifiers, we use as input the
classifications made by the HP Classifiers. Using the predicted labels
from the classifiers as the true labels, the patterns from test set ``HP test dev"
are extracted and compared to those patterns found in development set ``PE eval". We have
two feature sets for both sarcasm and nastiness: one using the
predictions from the MT indicators in the HP classifier ({\it percent
  features}) and another using those instances from the {\it $\chi^2$
  features}.

\subsection{Results from Pattern Classifier}

The Pattern Classifiers classify an utterance as Sarcastic (Nasty) if
at least two patterns are present and above the thresholds $\theta_1$
and $\theta_2$, exhausting all combinations of $\theta_1$ $=$ [2, 4,
  6, 8, 10] and $\theta_2$ $=$ [.55, .60, .65, .70, .75, .80, .85,
  .90, .95, 1.00]. The counter-classes are predicted when the
utterance contains less than two patterns.  The exhaustive classifications
are first made using the utterances in the development set labeled ``PE eval".
Fig.~\ref{param-fig} shows the precision and recall trade-off
for $\theta_1 =$ [2, 10] and all $\theta_2$ values on sarcasm development set``PE
eval".  As recall increases, precision drops. By including patterns
that only appear 2 times, we get better recall. Limiting $\theta_1$ to
10 yields fewer patterns and lower recall.

\begin{figure}[t]
\centering
\includegraphics[width=.5\textwidth]{train-recall-vs-prec}
\caption{Recall vs. Precision for Sarcasm PE eval \label{param-fig}}
\end{figure}

\pagebreak
Table~\ref{pc_eval} shows the results for various parameters.
The PE dev dataset learned a total of 1,896 sarcastic
extraction patterns above a minimum threshold of $\theta_1 < 2$ and
$\theta_2 < 0.55$, and similarly 847 nasty extraction patterns.
Training on development set ``PE dev" yields high precision and
good recall. To select the best parameters, we again look for a balance between
precision and recall. Both Classifiers have very high precision. In the end,
we select parameters that have a better recall than the best parameter
from the HP Classifiers which is $recall = 38\%$ for sarcasm and $recall = 49\%$
for nastiness. The best parameters and their test results are shown in
 Table~\ref{pc_final_resuts}.

\begin{table}[ht!]
\begin{scriptsize}
\begin{center}
\begin{tabular}{|p{0.40in}|r|c|c|c|c|}
\hline
 &  PARAMS  & P & R & F & N (tp)
\\ \hline \hline
SARC &  $\theta_1=$2, $\theta_2=$.60  & 65\% & 49\% & 0.57 & 792\\ \hline
 &  2, .65  & 71\% & 44\% &  0.58 & 717\\ \hline
 &  2, .70  & 80\% & 38\% & 0.59 & 616\\ \hline
 &  2, 1.0  & 97\% & 24\% & 0.60 & 382 \\ \hline
\multicolumn{6}{c}{} \\  \hline
NASTY &  $\theta_1=$2, $\theta_2=$.65  & 71\% & 49\% & 0.60 & 335\\ \hline
 &  2, .75  & 83\% & 42\% & 0.62 & 289 \\ \hline
 &  2, .90  & 96\% & 30\% & 0.63 & 209 \\ \hline

\end{tabular}
\caption{Pattern Classification Training; P: precision, R: recall, F: F-measure, tp: true positive classifications
\label{pc_eval}}
\end{center}
\end{scriptsize}
\end{table}

The Pattern Classifiers are tested on ``HP dev test" with the labels
predicted by our HP Classifiers, thus we have two different sets of
classifications for both Sarcasm and Nastiness: {\it percent features}
and {\it $\chi^2$ features}. Overall, the Pattern Classification
performs better on Nasty than Sarcasm.  Also, the {\it percent
  features} yield better results than $\chi^2$ features, possibly
because the precision for $\chi^2$ is high from the HP Classifiers, but the recall is
very low.  We believe that $\chi^2$ selects statistically predictive
indicators that are tuned to the dataset, rather than general.  Having
{\bf a human in the loop guarantees more general features} from a smaller dataset.
Whether this remains true on the size as the dataset increases to 1000 or more
is unknown. We
conclude that R\&W's patterns generalize well on our Sarcasm and Nasty
datasets (open question {\bf O3}), but suspect that there may be
better syntactic patterns for
bootstrapping sarcasm and nastiness, e.g. involving cue words or
semantic categories of words rather than syntactic categories, as we
discuss in Sec.~\ref{discuss-sec}.

\begin{table}[ht!]
\begin{small}
\begin{center}
\begin{tabular}{|p{0.5in}|r|c|c|c|c|}
\hline
& PARAMS & P & R & F    \\ \hline \hline
Sarc \% & $\theta_1=$2, $\theta_2=$.70 &   62\% & 52\% & 0.57 \\ \hline
Sarc $\chi^2$ & $\theta_1=$2, $\theta_2=$.70  &   31\% & 58\% & 0.45 \\ \hline \hline
Nasty \%  & $\theta_1=$2, $\theta_2=$.65 &  75\% & 62\% & 0.69 \\ \hline
Nasty $\chi^2$ & $\theta_1=$2, $\theta_2=$.65  & 30\% & 70\% & 0.50 \\ \hline
\end{tabular}
\caption{The results for Pattern Classification on HP dev test dataset \label{pc_final_resuts};
 PARAMS: the best parameters for
each feature set P: precision, R: recall, F: f-measure}
\end{center}
\end{small}
\end{table}

This process can be repeated by taking the newly classified utterances from
the Pattern Based Classifiers, then applying the Pattern Extractor
to learn new patterns from the
newly classified data. This can be repeated for multiple iterations.
We leave this  for future work.

\section{Discussion and Future Work}
\label{discuss-sec}

In this work, we apply a bootstrapping method to train classifiers to identify
particular types of subjective utterances in online dialogues.
First we create a suite of linguistic indicators for sarcasm
and nastiness using crowdsourcing techniques. Our crowdsourcing method is
similar to \cite{Filatova12}. From these new linguistic indicators we construct a classifier
following previous work on bootstrapping subjectivity classifiers
\cite{RiloffWiebe03,ThelenRiloff02}. We compare the performance of
the High Precision Classifier that was trained based on statistical measures
against one that keeps human annotators in the loop, and find that Classifiers using statistically selected
indicators appear to be overtrained on the development set because they
do not generalize well. This first phase achieves
54\% precision and 38\% recall for sarcastic utterances using the human
selected indicators. If we bootstrap by using syntactic patterns to create more
general sarcasm indicators from the utterances identified as sarcastic
in the first phase, we achieve a higher precision of 62\% and recall
of 52\%.

We apply the same method to bootstrapping a classifier for nastiness
dialogic acts. Our first phase, using crowdsourced nasty indicators,
achieves 58\% precision and 49\% recall, which increases to 75\%
precision and 62\% recall when we bootstrap with syntactic patterns,
possibly suggesting that nastiness (insults) are less nuanced and
easier to detect than sarcasm.

Previous work claims that recognition
of sarcasm (1) depends on knowledge of the speaker, (2) world
knowledge, or (3) use of context
\cite{Gibbs00,Eisterholdetal06,BryantFoxtree02,Carvalhoetal09}.
While we also believe that certain types of subjective language cannot
be determined from cue words alone, our Pattern Based Classifiers,
based on syntactic patterns, still achieves high precision and recall.
In comparison to previous monologic works whose sarcasm precision
is about 75\%, ours is not quite as good with 62\%. While the nasty works
do not report precision, we believe that they are comparable to the
64\% - 83\% accuracy with our precision of 75\%.

Open question {\bf O3} was whether R\&W's patterns are fine tuned
to subjective utterances in news. However R\&W's patterns improve both
precision and recall of our Sarcastic and Nasty classifiers. In future
work however, we would like to test whether semantic categories of
words rather than syntactic categories would perform even better for
our problem, e.g. Linguistic Inquiry and Word Count categories.
Looking again at row 1 in
Table~\ref{extract-pattern-fig}, ``It is quite strange to encounter someone in
this day and age who lacks any knowledge whatsoever of the mechanism of
adaptation since it was explained 150 years ago", the word `quite' matches the
`cogmech' and `tentative' categories, which might be interesting to generalize to sarcasm.
In row 11 ``At the time of the Constitution there weren't exactly vast suburbs that
could be prowled by thieves looking for an open window", the phrase
``weren't exactly" could also match the LIWC categories `cogmech' and `certain' or,
more specifically, certainty negated.

We also plan to extend this work to other categories of subjective
dialogue acts, e.g. emotional and respectful as mentioned in the
Introduction, and to expand our corpus
of subjective dialogue acts. We will experiment with performing more
than one iteration of the bootstrapping process (R\&W complete two
iterations) as well as create a Hybrid Classifier combining the
subjective cues and patterns into a single Classifier that itself can
be bootstrapped.

Finally, we would like to extend our method to different dialogue domains
to see if the classifiers trained on our sarcastic and nasty indicators would
achieve similar results or if different social media sites have their own style
of displaying sarcasm or nastiness not comparable to those in forum debates.

\bibliographystyle{naaclhlt2013}

redpajama_set_name: RedPajamaArXiv


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text:
The Cute Polka Dot Eastern Quolls Begin Their Reintroduction To Mainland Australia
In Animals by Anja, March 15, 2016
Eastern quolls haven't been seen on the Australian mainland for over half a century. However, thanks to a project that started this week, these polka dot marsupials could be making a comeback.
On March 1, researchers released 14 eastern quolls into Mulligans Flat Woodland Sanctuary, Canberra as part of a AUS $1.8 million (US $1,297,800) government project with the Australian National University, the James Cook University, and the Commonwealth Scientific and Industrial Research Organisation.
Although the species has continued to live happily on the island of Tasmania, they have not been seen in the wild on the mainland for more than 50 years. Once abundant in south-eastern Australia, eastern quolls (Dasyurus viverrinus) are skittish carnivores that grow to around 35 centimeters (13 inches).
"A Black Eastern Quoll" by Ways is licensed under CC BY 3.0
The introduced eastern quolls were transported from Tasmania in the space of one day. Over the next two years, the researchers will monitor the quolls using radio-tracking collars with the aim of introducing an additional 64 individuals: half will be captivity-bred in Victoria, and the other half from the wild in Tasmania.
The Eastern Quolls became victim to clearing, grazing, introduced predators and control programs
The reason for the eastern quoll's decline is unclear, according to the IUCN Red List. However, it has been speculated that an increase in roads and introduced predators, such as feral cats and red foxes, could be a factor.
"Eastern Quoll" by Michael Barritt & Karen May is licensed under CC BY 2.0
"The eastern quoll was once widespread across south-eastern Australia but became victim to clearing, grazing, introduced predators and control programs," Simon Corbell, the deputy chief minister of the Australian Capital Territory, told City News.
He added, "Despite being shy and nocturnal the introduction of this small carnivore could ultimately help with rabbit population control. Because of their role in helping to regulate prey species, quolls and other predators are disproportionately important in the functioning of ecosystems, so this is an important milestone in the sanctuary's ongoing restoration."
"Eastern Quoll" by sontag1 is licensed under CC BY 2.0
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Forested overpasses to help animals cross safely
Why You Should Avoid Palm Oil At All Costs

redpajama_set_name: RedPajamaCommonCrawl


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MANY countries around the world create far fewer jobs than they have workers for.
Singapore's problem is different - it has more jobs than it can find workers for.
A report by the Association of Small and Medium Enterprises (Asme) this month sums up the situation: "There are still many jobs available out there for locals but many SMEs still have difficulty recruiting local manpower in the market and hence find it difficult to meet the foreign labour quota."
Since the end of 2009, the Singapore economy has added between 20,000 and 37,000 new jobs every three months. This is not matched by the number of locals entering the workforce.
For decades, the city state has been heavily reliant on foreign labour to top up its local labour supply. But since 2010, the Government has tightened the inflow, as foreign worker numbers had grown so large as to put a strain on public transport and housing.
The trouble is, locals shun certain jobs - namely those that are labour-intensive, low-paid and tiring.
Asme, which represent SMEs that employ two in three workers here, says: "Many locals are not committed and are unwilling to work especially in the lower-tiered jobs, such as drivers, waitresses, and so on."
Sectors that rely on foreign workers to do such jobs have been hit by tighter foreign labour policy. From July, the share of foreign workers which service firms can have has been cut from 50 per cent to 45 per cent. In manufacturing, it is now 60 per cent, down from 65 per cent. Foreign worker levies have also gone up.
But even sectors which do not rely on foreigners are feeling the strain. The security sector, which hires only locals and Malaysians, has a shortage of at least 14,000 guards now.
Dishwashers made the news when restaurant chain Sakae Sushi said it could not find locals willing to wash dishes for 12 hours a day - with breaks - and six days a week, for $3,000 a month.
And in Parliament last week, Environment and Water Resources Minister Vivian Balakrishnan said Singaporeans will have to return their trays at hawker centres and "learn to cope with fewer cleaners".He added: "There is going to be a structural and chronic shortage of cleaners in the future."
THE demand for workers is not about to dry up any time soon.
The Government's own projections point to tens of thousands of new jobs over the medium term in sectors such as transport, housing, health care and education, to meet the needs of a growing and ageing population.
With the ramp-up of flat building by the Housing Board, the total number of construction workers needed could hit 45,000 in the next few years, up from 18,000 last year.
Of similar magnitude are the needs of the health-care sector. With two more public hospitals on the way, along with more community hospitals and nursing homes, the sector will need 35,000 more staff in the next eight years. These include doctors, nurses and attendants.
Other sectors which will need thousands more workers are eldercare, childcare and hospitality.
The 200 new childcare centres to be built by 2018, for instance, will call for 1,000 more teachers.
And with 10,400 more hotel rooms expected in the next four years, the sector will need about 7,000 more workers.
In a market where labour is scarce, wages rise. Workers who can, will move to sectors that pay better. Firms too may move - in search of lower-cost labour. Indeed, more than 60 per cent of SMEs in Asme's recent survey were thinking of relocating to neighbouring countries where labour, land and raw material costs are lower.
Construction material firm Eng Seng Cement Products has already done so. It set up a factory in Kota Tinggi in Malaysia last month to make prefab concrete walls for Housing Board flats.
The firm's director Chow Hoo Choong took Insight to his sprawling factory about the size of a soccer field in Kota Tinggi. The typical daily wage of a Malaysian worker is RM40 (S$16), he said, adding: "In Singapore, a worker costs at least three times more, provided we can find them to hire."
The migration and phasing out of companies that are unable to become more productive and less reliant on labour is precisely what economists say should happen as an economy restructures, not just at the level of firms, but that of industries.
Professor Hoon Hian Teck of the Singapore Management University notes that such restructuring dates back to the late 1970s, when labour-intensive industries such as textiles made way for higher value-added ones such as pharmaceuticals.
The Singapore Business Federation's SME committee has suggested a one-off government grant to help firms relocate to lower-cost countries, while maintaining links to Singapore.
Mr Lau Tai San, a member of the committee, highlights several other interim measures that firms think will help.
Allow companies to transfer their unused foreign worker quota to other firms in the same sector, so that the latter can, temporarily, have more foreign workers to ease their shortages.
"There is no shortage of ideas," says Mr Lau.
THE labour shortfall is here to stay.
Mr Lee Ark Boon of the Manpower Ministry (MOM), says Singapore's manpower crunch is "fundamentally a long-term issue" caused by a shrinking local labour force. "We have come to a point where our local working-age population is expected to start growing at a slower rate, and will eventually start to shrink within the next decade."
In May, the Institute of Policy Studies projected that if Singapore's total fertility rate stays at 1.24 and there are no new citizens or permanent residents, the local workforce will shrink from about two million in 2010 to 1.8 million by 2030, and below 1.5 million by 2045.
While immigration and foreign manpower add to the labour pool, the inflow has been tightened because of "social and infrastructural constraints", notes Mr Lee, who is MOM's divisional director for manpower planning and policy.
If a shrinking local workforce is not to translate into shrinking growth, either it will have to be topped up with foreign workers, or local workers must contribute more.
Asked about solutions, Mr Lee says: "The key long-term measure is to press on with economic restructuring and improving productivity."
The Government's target is for productivity growth of 2 per cent to 3 per cent a year this decade, though Acting Manpower Minister Tan Chuan-Jin said this month that remains a "stretch target".
Deputy Prime Minister Tharman Shanmugaratnam has indicated that results will come only with time, saying in April that economic restructuring would take "at least the better part of this decade" to complete.
A day later, the Monetary Authority of Singapore said Singapore might not reap the fruits of the productivity drive until "the second half of this decade".
To take a positive view, Singapore has significant scope to step up productivity. This is especially so "in the services sector, where the labour crunch is most severe", Mr Tharman said in his Budget speech this year.
For instance, Singapore's retail productivity is less than half of that in New York, Paris and London, and remains behind Hong Kong's, he said.
An economic simulation by National University of Singapore labour economist Hui Weng Tat estimated that if annual productivity growth was 3 per cent rather than 2 per cent, the demand for foreign labour in 2034 would be halved from 2.78 million - the figure it might be, given assumptions such as low total fertility - to 1.48 million.
THE productivity drive aims at making better use of every worker, which should reduce labour demand. But another, non-mutually exclusive measure is to raise labour supply - and foreign workers are not the only option.
One approach is to get more locals into the workforce.
Mr Lee of MOM says: "The Government is helping the economically inactive to go back to work and locals to work longer if they wish."
Firms are turning to retirees and housewives to fill gaps, and they are getting help from government agencies to do so.
Last month, the Singapore Workforce Development Agency and police launched a scheme to get retirees and housewives to work as security guards, by shortening their working hours.
Some SME bosses have proposed lowering the employee Central Provident Fund contributions of part-time workers, so as to boost their take-home pay and encourage more to work part-time.
The share of women and those aged above 60 in the workforce has risen in the last decade, as has the number of part-timers. The numbers are still small compared to the foreign worker population.
Economists themselves say in the light of a labour crunch that is here to stay, the questions facing Singapore go beyond economic theory.
The real questions concern the kind of society Singaporeans want, and the pace and quality of economic growth that society expects.
For Professor Basant Kapur of the National University of Singapore, how many workers - and foreign workers - are needed depends on the answers to these questions.
"After factoring in feasible growth of the local labour force, productivity growth and so on, we should ask: For any given level and composition of foreign worker inflow, what will be the resulting estimated growth of labour costs in Singapore, and what will be the resulting estimated GDP (gross domestic product) growth rate?"
That will give a better sense of the trade-offs between foreign labour inflows, costs and growth, he says. And from this, perhaps "policymakers and the public at large as part of the Singapore Conversation" can arrive at a consensus on how best to tackle the challenge of an economy with too many jobs, and too few workers.


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XHPEBX-FM is a radio station on 95.7 FM in Cintalapa de Figueroa, Chiapas. The station is owned by Erasmo Ángel Ruiz and is a Christian station known as Radio Estrella.

History
Ciencia, Comunicación y Tecnología filed for a permit station in Cintalapa de Figueroa on August 27, 2009. The application was filed 17 minutes apart from one by Amadeo Coutiño Aguilar. While the IFT had initially planned to award just one station on April 25, 2018, due to the fact that the applications were filed within the same hour, the IFT opted to award both stations and reject a 2013 application by Promoción y Fomento Cultural, A.C. As the Coutiño Aguilar station, awarded as XHPEAQ-FM, had been filed earlier, this unanimous decision by the IFT's commissioners allowed Radio Estrella to become a legal station.

XHPEBX went on the air in May 2019, relocating religious pirate station Radio Estrella from 95.9 MHz, where it had operated since at least 2011.

References

Radio stations in Chiapas
Radio stations established in 2019
Former pirate radio stations
2019 establishments in Mexico
Radio stations established in 2009

redpajama_set_name: RedPajamaWikipedia


Ear candling is a practice you've no doubt heard of. Before you give it a try, check this article. Does ear candling work? Here is the answer.
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Humboldt provides sand cone density apparatus for soil density testing.
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1. Place the base plate on a clean, level, plane surface. Invert the sand cone filled with sand, and seat the funnel in the recess of the base plate.


text:
Best Episode of the Season: Desperate Housewives Season (Final Season!)
jmunney Best Episodes of the 2011-2012 Season, Best Episodes of the Season, Desperate Housewives, Television Leave a comment
Season Analysis: The final season of Desperate Housewives suffered from a setup in which the four main ladies acted in a way that I just never bought.
Though he did reprise the role of Orson for a few episodes, Kyle MacLachlan was not the best Twin Peaks alum on Desperate Housewives' final season. That honor goes to Miguel Ferrer as Andre Zeller, Susan's grumpy, exacting art teacher, and it was his presence that served to make some of the best moments of the season as good as they were, and he is a major reason for my pick of "Always in Control" as the best episode of the season. Individual episodes of Desperate typically just continue the storylines from the previous episodes, with the characters from separate storylines maybe – or maybe not – bumping into each other, so it is rare – or at least it has been rare the past few seasons – for an entire episode to distinguish itself. "Always in Control" gets my vote, then, not because the whole episode was great, but because it climaxed in a moment for which I had been holding out hope for several weeks: Susan finding it in herself to actually produce a good painting, prompted by her discovery that the other girls had been hiding the news that Alejandro's body had been dug up. Andre's response to her good work was the same as mine: "Finally."
Best Episode of the Season: Desperate Housewives Season 7
"Farewell Letter"
During the early days of Desperate Housewives, Gabby was the weakest of the housewives, her storylines less meaningful and even frothier than those of the other housewives. Now that the show as a whole has grown a little long in the tooth, the situation has reversed, and Gabby's storylines now have the most emotional depth of the series. Take "Farewell Letter" for example, in which Gabby and Carlos returned to Gabby's hometown to confront the demons of her childhood. She confronted the nun who had never – and still refused – to believe Gabby's revelations of her stepfather molesting her. Anyone who has grown up Catholic knows that confronting a nun – particularly a really mean nun – is a powerful situation. This moment was just as wrenching as Gabby's hysterics following the loss of Grace, but it was also fulfilling, and the writers had worked hard to get to that fulfillment.
Next up: How I Met Your Mother
Best Episode of the Season: Desperate Housewives
"I Guess This is Goodbye"
In a season rife with a pair of mysteries we were not sure we wanted (the Fairview Strangler and the Bolens on the run), we were happy to at least have satisfying resolutions to them, making the season finale Desperate's most thrilling and most explosive hour of the year, as per usual. The Fairview Strangler ended with Eddie delivering Lynette's baby and Lynette in turn convincing Eddie to turn himself in to the police. Josh Zuckerman put Eddie's internal struggle all over his face, and Felicity Huffman had one of her best mom moments of the series. Scott Wolf-lookalike John Barrowman was impressive as Patrick Logan, and watching Angie blow him up was the funnest moment of the season, and one of the few times when Drea De Matteo was given something interesting to do. And though it was sad to know that he was on his way out, Kyle MacLachlan finally had the opportunity to remind us why we have loved him as Orson Hodge so much, with his speech to Bree in which he explained why he loved her and why he cannot love her anymore.
Next up: American Dad!


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Niqāb (egl. [ansigts] 'slør', moderne dansk retskrivning niqab, uofficiel stavemåde: nikab, ) er et slør der dækker hele ansigtet undtagen øjnene, og som bæres offentligt af nogle muslimske kvinder sammen med et tørklæde som del af en påkrævet islamisk klædedragt.<ref>Den Danske Ordbog (DDO) (1955–), opslag på niqab online.</ref> Niqab er udelukkende et ansigtsslør, der slås om hele hovedet og dækker alt undtagen øjnene.

Der skelnes generelt mellem ansigtsslør (niqab), som kun lader en sprække fri til øjnene, mens tørklæde (ḥijāb) almindeligvis betegner et hovedslør, der først og fremmest dækker hår, nakke og hals. Mere generelt kan hijab også betegne tilsløring og tildækning af kroppen. Hijab i denne brede betegnelse betragtes som det beklædningsstykke, der lever op til minimumskravene for islamisk tildækning af piger og kvinder, hvor ansigtssløret (niqab) iblandt størstedelen af de islamiske lovskoler anses som tilrådet og ikke pligtig.

Ansigtssløret niqab må ikke forveksles med de forskellige former for tilsløring. I Iran benyttes traditionelt den sorte chador (et stort sjal eller kappe, der lægges om hovedet), i store dele af Golfstaterne abaya (der dækker hele kroppen, men er åbent ved ansigtet), i Pakistan purdah, i Saudiarabien niqab'' og i Afghanistan burka, alle former for beklædning, der dækker hoved, krop og til dels ansigt fuldstændigt. Hertil knyttes at der i det sydlige Iran og i landområder med beduiner som Oman og Abu Dhabi, praktiseres også tildækning af ansigtet med en stof- eller lædermaske, der helt eller delvist dækker ansigtet.

Maskeringsforbud
Niqab og burka som påklædningsgenstande har været i særlig fokus i den danske debat siden 2017, som et forslag er blevet præsenteret til at disse typer af beklædning skal falde under maskeringsforbud, med henvisning til straffelovens paragraf 134.

Oprindelse
Niqaben stammer oprindeligt fra beduinkulturer på Den Arabiske Halvø, hvor beklædningen allerede i førislamisk tid blev anvendt af både kvinder og mænd som værn mod sand og sol. Den blev også en typisk kvindebeklædningsdel i det kristne, før-tyrkiske Byzans (med Konstantinopel), af fornemme kvinder ofte båret med maske.

Beklædning
Niqab fås normalt mellem et til tre lag slør, hvoraf det inderste lag normalt har sprække til øjnene, evt. adskilt af en lille snor. Det ydre lag, som kan slås tilbage over hovedet, er et slør, der dækker øjnene, så kvinden kan se. Af dette lag kan der være flere. Niqaben bindes i nakken (som tildækkes med hijaben). Niqaben kan være af silke eller syntetisk materiale, ofte nylon (polyester), eller en blanding heraf.

Muslimske kvinder forsøger på meget forskellig vis at efterleve traditionens krav om slør og tildækning - her kan der også tales om skiftende modetendenser.

Se også
 Chador
 Hijab
 Burka
 Tørklæde

Referencer

Eksterne henvisninger

Islam
Kåber
Arabiske ord
Hovedbeklædning
Religiøs beklædning


text:
Q: Why is this private constructor inaccessible to its friend #include <iostream>

using namespace std;

class B {
private:
  class A;
  friend void f ( A Aobj );
  B ( int i ) {}
};

class A{
};

void f ( A Aobj ) {
  B Bobj ( 1 );
}

int main() {
}

g++ produces the following error:
$ g++ a.cpp
a.cpp: In function 'void f(A)':
a.cpp:10: error: 'B::B(int)' is private
a.cpp:18: error: within this context

The error goes off if any of the following changes are made:
1. Remove the "int i" from the constructor of B.
2. Change the data type of f from A to anything else: eg void f ( int Aobj ).
3. Define class A prior to B and remove the forward declaration of A.

A: The problem is that the friend function takes a B::A, where the function later on in the file takes a A. Move the forward declaration of class A out of B.

A: You've declared the constructor of B - the function B (int i){} as a private method. You want to make this public, like so:
class B {
private:
  class A;
  friend void f ( A Aobj );
public:
  B ( int i ) {}
};

**EDITED
If you want to keep the constructor private, that shouldn't be an issue. Looking at it again, I realize the error lies with the forward declaration of the class A. You've declared A in the class definition of B, and then try to use it in the function header for f. The problem is that you haven't defined A fully, so you cannot reference the object yet - the compiler doesn't know how much memory A takes up, so the function cannot be defined. To fix this, you either need to fully define class A before any reference to it, or switch f to take a pointer to A (i.e. void f (A* Aobj)). Pointers are a fixed size, so they can be used even if A hasn't been defined fully yet.

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text:
Marvel - The Cinematic Universe
December 26, 2018 / Wayne Murphy
I did cover Marvel way back in 2015, back when this was just little one page blog over at Wayne-wonder.com but I think with the passing of Stan Lee and now that we have so many more great characters to have fun with it's time to delve deeper and expand on what we had already.
Now I know the man wasn't perfect, I know he took quite a lot of credit that wasn't his to take and some ummm "inspiration" from DC characters, look nobody is perfect but he did keep Marvel going, he didn't sell it to Michael Jackson and he did end up creating something truly unique, truly remarkable and something that is the envy of Hollywood.
He allowed and encouraged the creation of Marvel's Cinematic Universe which I think will be ultimately what he will be remembered for by the masses. We are going to have lots of fun with this series of Marvel posts, As I write this I don't know how many posts I am going to write but its going to be a whole bunch, I think I am going to try and team up different hero's and create some fun games, crafts activities and party ideas around them in each blog post. As we build on our Marvel ideas I will add more links here but make sure you check out these great fun ideas all based around different Marvel Characters.
Thor Fun & Games
Spider-Man Fun & Games
Captain America Fun & Games
Ant-man Fun & Games
Iron Man Fun & Games
Black Panther Fun & Games
At this point with 20 films, most of the world has heard of the Marvel Cinematic Universe (MCU for short) and many people have questioned why the MCU continues to thrive and bring in more and more people when other cinematic universes go belly up.
A Cinematic Universe is something no-one ever thought possible, let alone to become the biggest franchise the world has ever seen. You may be able to argue that some others have attempted it like The Cornetto Trilogy, Tarantino or maybe even Godzilla but I don't think any of them match what Marvel has created. One of the key differences between a regular franchise, such as The Fast and the Furious or Pitch Perfect films, and a shared universe is the amount of planning and interweaving that goes into each individual film. Its all too easy to make a film that exists solely for the purpose of setting up future installments and expanding a world, rather than a film that stands on its own merits while deftly hinting or winking at its place in the larger mythos.
There are many reason for the success of the MCU, but I think two names come too mind for me straight away and they are Kevin Feige and Joss Whedon. Kevin Feige's planning and adaptation of the source material has been second to none, there is no way this would have been a success if it wasn't for him.
Joss Whedon, I think has had the biggest impact to the look and feel of the universe, he wrote the film treatment for X-Men, co-wrote Captain America: The First Avenger and Thor: The Dark World, co-wrote and directed of The Avengers and Avengers: Age of Ultron. He also co-executive produced and co-created Agents of S.H.I.E.L.D. with his brother Jed and sister-in-law Maurissa Tancharoenand co-wrote the "pilot" episode.
One of the elements I love so much with the MCU is the planning in Phases. I don't know if this was planned marketing or it just happened to work but Marvel up to this point keep telling everyone what their plans are (kind of) for each phase. They will announce which characters and films they are working on for every new phase, this then generates a huge buzz and excitement from the fans wondering how these characters will fit into the MCU and what will their relationships be with the other characters. For me it was the announcement of Antman, now I have to make a confession. Growing up I was always more of a DC fan and would mainly read DC comics, I preferred the darker story lines and I'm a big Batman fan so I had never heard of Antman until Marvel announced phase 3 and I was thinking oh well that's that, its been a good ride but Marvel has run out of steam. I was wrong, Paul Rudd is perfect casting and I think after Guardians of the Galaxy it's probably my favourite film. Oh and I loved Thor Ragnarok and Spiderman Homecoming and Civil War and the Avengers films oh I just love the MCU.
When watching Marvel movies from their Phase One, movies coming out between Iron Man (2008) and The Avengers (2012), you notice something that is extremely different than the DC Extended Universe (DCEU) and Universals Darkness Universe — they weren't planning for the future.
Christoper Nolan's Batman Begins came out in 2005, showing everyone that superhero movies can be cool and Kevin Feige took this chance to produce the start of a new era of superhero movies. With a budget of 140 million, I don't think anyone expected it to make 4 times that amount back with a total gross of 585.2 million dollars.
I am super excited about the future of the MCU with Captain Marvel, Spiderman Far From Home, Avengers 4 End Game, Black Panther 2 and hopefully Guardians of the Galaxy 3 sadly without James Gunn at the helm.
Hopefully this gives you a good introduction to the MCU, some history of it and where it's going over the next year, I think this first post is long enough but make sure you come back soon for our first superhero team which is going to be Thor and Iron man, we are going to share some great fun ideas for them and then we are going to keep adding more fun with more characters until you tell me to stop.
Now if you are new to the MCU or you may be introducing your little one to the awesomeness of the MCU? I have included below a brilliant timeline for all the films so far, I hope that I am able to update this as new films come out and I can't wait to see where Captain Marvel fits in. This order, I think is the best way to watch the films. I have also included the Agents of Shield TV show because it features characters that also appear in the films and it sometimes deals with situations and events from the films too but don't forget all the Marvel shows on Netflix too.
We have also included below two brand new invites to add to our Party invite Library, we have the Marvel Movie Marathon (Stan Lee alliteration) invite for having some friends round to eat snacks and watch Marvel Movies plus we have the great Marvel Party Invites for kids Birthday Parties too.
Marvel - Dr Strange and Thanos
Superhero's everywhere! I really love the MCU, obviously it cannot go on forever but for now it is just awesome, this week we are going to continue our fun on the MCU by looking at Doctor Strange and even Thano's, ok he's not a super hero but he is a brilliant villain and where would all these awesome superhero's be if they didn't have a worthy adversary.
Marvel - Incredible Hulk and Loki
Lets kick off our Hulk and Loki fun as we always do with these Marvel posts, my favourite Hulk clip, what a great character and this clip is just played perfectly by Tom Hiddleston, Puny God!
Marvel - Guardians of the Galaxy and Captain Marvel
We're back with another instalment of Marvel fun. This time we are going to explore space with Guardians of the Galaxy and Captain Marvel which is pretty difficult to do as no-one has yet seen the new Captain Marvel film, it's coming out on March 8 this year so in a couple of weeks we will know what this awesome trailer is all about.
Marvel - Spider-man and Black Panther
Hello and welcome to our next post in our Marvel series, make sure you use the search bar above and type in Marvel to find all of our Marvel fun. This week we are going to explore some fun with our friendly neighbourhood Spiderman and Black Panther. We are always going to kick off with a little clip of the superhero's in action so here is a brilliant clip from Captain America Civil War featuring both Spiderman and Back Panther.
Marvel - Captain America & Ant-Man
Welcome back to our complete guide on Marvel, make sure you check out our previous posts covering Marvel too, I have included them all at the bottom of this post. We are teaming up super heroes and then finding some cool fun things that you can do with the kids about those super heroes so this week we have Captain America and Ant-Man.
Marvel - Iron Man and Thor
This week I am going to show you some great fun ideas for Iron and and Thor. We are going to try and go through every super hero in the MCU stable so make sure you come back regularly for more super hero fun.
Big Hero 6 Activity Pack
If you haven't seen Big Hero 6 yet than go and get from your local library. For me it was a spontaneous trip to the cinema with my daughter and the only thing playing at the time was Big Hero 6, it is a brilliant adaptation of a Marvel comic that never really caught on. I have included some of my favourite clips below
December 26, 2018 / Wayne Murphy/ 2 Comments
Birthday Parties, Disney, Free Downloads, Indoors, Party Invitations
Marvel, MCU, Avengers, Black Panther, Guardians, Thanos
Wayne Murphy
professional Entertainer, writer and creative junkie
Inside Out - Games


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White vinegar is a brilliant way to clean shower screens and remove limescale. Put full strength white vinegar in a spray bottle and spray on the shower screen. Use a non-scratching cloth to wipe the shower screen and then rinse it off with warm water. Most households have bicarbonate of soda in the kitchen. Bicarbonate of soda will not only clean shower screens, it's a great natural... You should clean your showerhead as often as necessary, but definitely do a thorough cleaning once a year or so depending on how hard your water is. In between full cleanings, you can fill a Ziploc bag with vinegar and attach it to the shower head without removing it.
6/08/2007�� If you have hard water, don't waste money on CLR when you can just as easily soak a limed up showerhead in a jar of vinegar overnight , then add a �... If you need to, flush out the showerhead with hot water and repeat the process until it�s clean. It may take a few times if you have mineral build up. It�s especially bad with well water. It may take a few times if you have mineral build up.
If you need to, flush out the showerhead with hot water and repeat the process until it�s clean. It may take a few times if you have mineral build up. It�s especially bad with well water. It may take a few times if you have mineral build up.... 7/04/2008�� Spray CLR or LimeAway (CLR works better) into the shower head...let it sit for a few minutes, scrub with a brush or old toothbrush... Then run the water through it (making sure it does not get on you as the chemicals are in it).


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While the rear of this sofa shows the Chesterfield, with its rows of tufting, a tux couch has solution, more angular outlines. It is said to possess been the bellwether of more modern designs in the 20's. Although some say this sofa style took its title from Tux Recreation area in Ny, it's also considered named following the classic extravagant men's suit. You can identify a tux sofa by its hands that are the same height as the back again. The tufting on the back of the couch and its rectangular silhouette are also traditional characteristics. Cushions add comfort and ease to this higher-armed design. Also, as the sofa over from Upcountry is upholstered in leather, this popular design is usually completed in textiles of all kinds, including the very trendy velvet.
Fluffy, overstuffed loveseat cushions are not your thing? This reclining loveseat from GDF has sharper, cleaner outlines than your conventional reclining loveseat, making it a stylish accessory for a middle-century contemporary, contemporary, modern or modern type of house. And, at just 46.46 inches in width, it's very easy to squeeze into a smaller room, like flats, dens or offices. Obtainable in a number of color and fabric options, such as charcoal material, navy blue material and slate micro-fiber, its easy to find the perfect complement for your house dcor, and the sturdiness your loved ones needs for daily use. The days are gone of good-looking, but unpleasant furniture you will no longer have to give up comfort and ease for design. This lying loveseat functions a big, gentle, luxurious-stuffed seat cushioning with sufficient room for 2 or enough room for one to spread out and unwind.
Just like its namesake, the camelback couch has a hump or possibly two that highlights the primary silhouette of the couch style. This is a traditional design that was prominent within the 18th century by Chippendale and households who desired aristocratic, official furniture often chosen this design. Today, this sofa design lends a far more official atmosphere to any space, especially when upholstered inside a formal material. A far more informal fabric choice will make it appropriate for a family space that doesnt need a lot of custom. Either way, the camelback sofa has some factors that define the style. The legs are typically uncovered, the sofa has no back cushions and it usually has sq . or rolled arms.
This is definitely the cutest and cleverly created loveseat. Contingent within the tone, it gives a disturbing appearance to your office or rooms. The sofa unimaginably imperceptible for a morning java or an computer animated magazine reading very carefully. It is accessible in leather and in consistency depending on what you support a bright appear, or perhaps a lovely, lovely look at. The best part, it's very reasonable! The extremely chic tufting is molded stone, which holds an ideal Chesterfield style. Probably the most lovable point about this couch is its shape. Along with the appeal, the trunk style gives the back knowledge of relaxation. It is skillfully designed to ensure that smaller sized rooms combine luxury inside a smaller sized space. It is a smart choice for that childrens room, as it offers a number of sweet tones. They would like to hit them with the storyline or a fantastic dream. No hassle for grown ups as well for a comfortable evening with Manga or Wonders.
John RTA Copenhagen Selection 61 Loveseat in Marzipan is designed specifically for little areas. Nonetheless, you can use it in almost any room: condo, dorm and so on. This set provides you with more than enough assistance. It boasts of high quality fabric, higher denseness foam pocketed circles, leading-collection polyester dietary fiber, and powerful hard wood body. This increases the comfort, durability, and sturdiness of this set. The set also includes an ultra wide back again shoulder straps. The established also includes extra comfy cushions all which makes it much more comfortable. Notably, it has gently rounded arms and luxurious material allow it the outstanding look that matches with any style. The established checklist compact, therefore it's very easy to transport. It features a slight scent that fades with time. More to the point, the sofa is extremely simple to put together. It includes the putting together resources and directions. You easily commit your cent hear. It serves the reason.
This suits you if all you need is a beautiful coach. It is designed with a flared frame and cushion leading amrests inside a cool cobblestone gray. Now, it's resilient froth soft cushions that are wrapped up in in a polyester upholstery to bring about the desired comfort and ease. It features a durable corner obstructed body that adds to the toughness. Also, feet are in a fake wood finish. We have an remarkable grey colour that matches with any decoration. It measures 89 T by 39 Deb x 40 H therefore big enough to support you along with your loved onesOrfriends. More to the point, it arrives completely assembled. This protects the agony of getting to assemble the set.
If youre looking for some severe rest, this loveseats solid pillow back again and rolled hands will deliverbut at a higher cost. Every seat cushioning consists of 30 person pocketed coils for spring and support, and high strength seat froth for an extra coating of plushness and sturdiness. Plus, the foam cushions are capped with extremely gentle, blended lower feathers. For the additional comfort, there are 3 standard positions: sitting, a small recline by having an raised footrest, along with a full lie down with an raised footrest. It does not hurt that this reclining loveseat appears lux, possibly. Its all-leather furniture and decorative toe nail head trim give it a vintage look that can function with lots of home decorations designs, from traditional to nation to arts and crafts. Plus, leather stands up nicely towards daily use, children and pet locks and fingernails. Just remember to situation the leather-based every 6 to 12 months.


There are no two ways about it - Ole Gunnar Solskjaer's reign as Manchester United caretaker manager has been nothing short of exceptional thus far. "We must be concentrated and strong, positive to show our personality during the game".
Lingard has been one of several players to benefit from the arrival of Solskjaer.
Solskjaer replaced Jose Mourinho in December and has guided United to seven wins and a draw in the Premier League to make a compelling case for his appointment ahead of early bookmakers' favourite Pochettino.
Claudio Ranieri does not think Fulham's upcoming clash with Manchester United is a foregone conclusion, pointing out the Red Devils' unbeaten run must end at some point.
Rooney was the embodiment of Sam Allardyce's stodgy Everton era; a slow-paced player for a side who needs more dynamism than that.
The calming and edifying presence of Solskjaer has worked wonders for a kid who, under Mourinho, looked petrified in the box a lot of the time, clearly conscious of the retribution in store for him from his former manager.
Solskjaer's side face Fulham in the league on Saturday.
Lingard lifted the lid on how the Manchester United pair have been setting each other targets in games.
The Mirror suggest that United's executives are growing increasingly pessimistic about the possibility of snaring Mauricio Pochettino from Tottenham, with the Argentinian conveying mixed messages after pledging his loyalty to Spurs, whilst simultaneously stating his desire to compete for silverware on a regular basis.
This means that the Norwegian has the upper hand when it comes to the inner workings of the club, meaning that he could get on with things straight away when he took over from Mourinho.
Molde managing director Oystein Neerland told Sportsmail last month that there is no agreement beyond May 12 for Solskjaer to remain at United. He was a fantastic player.


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Dr. Jennifer Weiss, Partners Offer Expertise to New Statewide Health Disparities Report
Recognizing that where people live affects their health, Jennifer Weiss, MD, MS, associate professor, Gastroenterology and Hepatology, and partners at the University of Wisconsin Health Innovation Program (HIP) have developed a more nuanced way to identify health disparities in Wisconsin's rural and urban areas. Their work is reflected in "WCHQ...
Research - Recent Publications
New Faculty Q&A with Luke Hillman, MD
Luke Hillman, MD Assistant Professor Gastroenterology and Hepatology What is your primary professional focus in any or all of our mission areas (clinical, research, and/or medical education)? My primary professional focus is helping patients with esophageal diseases including gastroesophageal reflux disease, eosinophilic esophagitis and motility disorders such as achalasia. When...
Madeline Rastall, MS
Drs. Ann Sheehy and Amanda Parkes Receive UW Health Physician Excellence Awards
UW Health recently recognized physicians and advanced practice providers (APPs) who have demonstrated exceptional performance in clinical practice, education or regional services and displayed a commitment to the UW Health mission, vision and values. A number of physicians and APPs from the Department of Medicine were nominated, with two award...
Getting to Know You: Q&As from New Faculty and Chief Residents Joining the Department of Medicine in 2019-2020
Vital Signs is featuring introductions to many of the new faculty members and chief residents who joined us in the 2019-2020 academic year. Each has been invited to provide some background about their primary professional focus in clinical, research, and/or medical education, what drew them to UW-Madison, their hopes and...
New Faculty Q&A with Andrew Spiel, MD
Andrew Spiel, MD Assistant Professor Division of Gastroenterology and Hepatology What is your primary professional focus in any or all of our mission areas (clinical, research, and/or medical education)? My focus in joining the faculty is twofold. I care about education and value the institution responsible for most of my...
New Faculty Q&A with Andrew Hryckowian, PhD
Andrew Hryckowian, PhD Assistant Professor Division of Gastroenterology and Hepatology What is your primary professional focus in any or all of our mission areas (clinical, research, and/or medical education)? My primary professional focus is on research. In my laboratory, we study the microbial communities (microbiomes) that inhabit our bodies, with...
U.S. News Rankings: University of Wisconsin Hospitals Again No. 1 in Wisconsin; Six Department of Medicine Subspecialties Represented
UW Health's hospitals and providers have earned a reputation for innovation and remarkable patient care that is unmatched in the state and recognized throughout the world. For the ninth year in a row, U.S. News & World Report's "Best Hospitals" ranked University of Wisconsin Hospitals, which includes University Hospital and...
Faculty Promotions for 2020
Congratulations to the 27 Department of Medicine faculty who were approved for promotion this year. Their new ranks went into effect on July 1, 2020. Clinical Track Associate Professor Peter Gill, MD, Hospital Medicine Joseph Holt, MD, Hospital Medicine Kraig Kumfer, MD, PhD, Hospital Medicine Muhammad Shahzeb Munir, MD, Hospital...
Philanthropic Gifts and Messages in Honor of Doctors' Day 2020
Recently, the medical advancement group at the Wisconsin Foundation and Alumni Association sent a mailing to patients in celebration of National Doctors' Day on March 30, 2020. It offered patients the opportunity to honor their physician through a philanthropic gift or a thank-you message. Gifts received will be used to...
Department Faculty Receive Notes of Gratitude for National Doctors' Day
Recently, UW Health offered an opportunity for people to submit notes of gratitude to their physician in celebration of National Doctors' Day on March 30, 2020. One hundred thirty-five Department of Medicine physicians across all divisions received messages. We're proud to share excerpts of some of them here. Allergy, Pulmonary...
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As president of the Israel Diamond Exchange for nearly 30 years, Schnitzer helped boost polished diamond exports, which rose from $200m. in 1967 to $3.4b. in 1993.
Moshe Schnitzer, one of the founding fathers of the Israeli diamond industry who served as the president of the Israel Diamond Exchange and the World Federation of Diamond Bourses during his 65-year legacy in the business ,died over the weekend at the age of 86. Schnitzer, born in Romania in 1921, immigrated to Palestine in 1934 and entered the diamond industry at the age of 21. Just five years later Schnitzer emerged as one of the founding fathers of what became the Israel Diamond Exchange. As president of the Israel Diamond Exchange for nearly 30 years, Schnitzer helped boost polished diamond exports, which rose from $200 million in 1967 to $3.4 billion in 1993. He was a major proponent of the establishment and expansion of the four-building diamond exchange complex in Ramat Gan, which is today considered the largest in the world. In 2004, Schnitzer was awarded the Israel Prize for his life's work and contribution to Israeli society.