text:
\section{Introduction}

Recently, deep learning methodologies have dominated several research areas in natural language processing (NLP), such as machine translation, language understanding, and dialogue systems.
However, most of applications usually utilize word-level embeddings to obtain semantics.
Considering that natural language is highly ambiguous, the standard word embeddings may suffer from polysemy issues.
\newcite{neelakantan2015efficient} pointed out that, due to triangle inequality in vector space, if one word has two different senses but is restricted to \emph{one embedding}, the sum of the distances between the word and its synonym in each sense would upper-bound the distance between the respective synonyms, which may be mutually irrelevant, in embedding space\footnote{$d(\text{rock},\text{stone})+d(\text{rock},\text{shake}) \geq d(\text{stone}, \text{shake})$}.
Due to the theoretical inability to account for polysemy using a single embedding representation per word, multi-sense word representations are proposed to address the ambiguity issue using multiple embedding representations for different senses in a word~\cite{reisinger2010multi,HuangEtAl2012}.

This paper focuses on unsupervised learning from the unannotated corpus.
There are two key mechanisms for a multi-sense word representation system in such scenario:
1) a sense selection (decoding) mechanism infers the most probable sense for a word given its context and
2) a sense representation mechanism learns to embed word senses in a continuous space.

Under this framework, prior work focused on designing a single model to deliver both mechanisms \cite{neelakantan2015efficient,li2015multi,Qiu2016ContextDependentSE}.
However, the previously proposed models introduce side-effects:
1) mixing word-level and sense-level tokens achieves efficient sense selection but introduces ambiguous word-level tokens during the representation learning process~\cite{neelakantan2015efficient,li2015multi}, and 2) pure sense-level tokens prevent ambiguity from word-level tokens but require exponential time complexity when decoding a sense sequence \cite{Qiu2016ContextDependentSE}.

Unlike the prior work, this paper proposes \emph{MUSE}\footnote{The trained models and code are available at \url{https://github.com/MiuLab/MUSE}.}---a novel modularization framework incorporating sense selection and representation learning models, which implements flexible modules to optimize distinct mechanisms.
Specifically, MUSE enables linear time sense identity decoding with a \emph{sense selection module} and purely sense-level representation learning with a \emph{sense representation module}.

With the modular design, we propose a novel joint learning algorithm on the modules by connecting to a reinforcement learning scenario, which achieves the following advantages.
First, the decision making process under reinforcement learning better captures the sense selection mechanism than probabilistic and clustering methods.
Second, our reinforcement learning algorithm realizes the first single objective function for modular unsupervised sense representation systems.
Finally, we introduce various exploration techniques under reinforcement learning on sense selection to enhance robustness.

In summary, our contributions are five-fold:
\begin{compactitem}
  \item \emph{MUSE} is the first system that maintains purely sense-level representation learning with linear-time sense decoding.
  \item We are among the first to leverage reinforcement learning to model the sense selection process in sense representations system.
  \item We are among the first to propose a single objective for modularized unsupervised sense embedding learning.
  \item We introduce a sense exploration mechanism for the sense selection module to achieve better flexibility and robustness.
  \item Our experimental results show the state-of-the-art performance for synonym selection and contextual word similarities in terms of MaxSimC.
\end{compactitem}

\section{Related Work}

There are three dominant types of approaches for learning multi-sense word representations in the literature: 1) clustering methods,  2) probabilistic modeling methods, and 3) lexical ontology based methods.
Our reinforcement learning based approach can be loosely connected to clustering methods and probabilistic modeling methods.

\citet{reisinger2010multi} first proposed multi-sense word representations on the vector space based on clustering techniques.
With the power of deep learning, some work exploited neural networks to learn embeddings with sense selection based on clustering~\cite{HuangEtAl2012,neelakantan2015efficient}.
\citet{chen2014unified} replaced the clustering procedure with a word sense disambiguation model using WordNet~\cite{miller1995wordnet}.
\citet{kaageback2015neural} and \citet{vu2016k} further leveraged a weighting mechanism and interactive process in the clustering procedure.
Moreover, \citet{guo2014learning} leveraged bilingual resources for clustering.
However, most of the above approaches separated the clustering procedure and the representation learning procedure without a joint objective, which may suffer from the error propagation issue.
Instead, the proposed approach, MUSE, enables joint training on sense selection and representation learning.

Instead of clustering, probabilistic modeling methods have been applied for learning multi-sense embeddings in order to make the sense selection more flexible,
where \citet{tian2014probabilistic} and \citet{jauhar2015ontologically} conducted probabilistic modeling with EM training.
\citet{li2015multi} exploited Chinese Restaurant Process to infer the sense identity.
Furthermore, \citet{bartunov2015breaking} developed a non-parametric Bayesian extension on the skip-gram model~\cite{mikolov2013distributed}.
Despite reasonable modeling on sense selection, all above methods mixed word-level and sense-level tokens during representation learning---unable to conduct representation learning in the pure sense level due to the complicated computation in their EM algorithms.

Recently, \citet{Qiu2016ContextDependentSE} proposed an EM algorithm to learn purely sense-level representations, where the computational cost is high when decoding the sense identity sequence, because it takes exponential time to search all sense combination within a context window.
Our modular design addresses such drawback, where the sense selection module decodes a sense sequence with linear-time complexity, while the sense representation module remains representation learning in the pure sense level.

\begin{figure*}
\centering
  \includegraphics[width=1\linewidth]{model_architecture.pdf}
  \vspace{-4mm}
  \caption{The \emph{MUSE} architecture with a 3-step learning algorithm: 1) collocation sampling, 2) sense selection for sense representation learning, and 3) optimizing sense selection with a reward signal from sense representation. Reward signal is only passed to the target word to stabilize model training due to directional architecture in the sense representation module.}
  \label{fig:model}
  \vspace{-2mm}
\end{figure*}

Unlike a lot of relevant work that requires additional resources such as the lexical ontology~\cite{pilehvar2016conflated,rothe2015autoextend, jauhar2015ontologically,chen2015improving,iacobacci2015sensembed} or bilingual data~\cite{guo2014learning,ettinger2016retrofitting,vsuster2016bilingual},
which may be unavailable in some language,
our model can be trained using only an unlabeled corpus.
Also, some prior work proposed to learn topical embeddings and word embeddings jointly in order to consider the contexts~\cite{liu2015learning,liu2015topical}, whereas this paper focuses on learning multi-sense word embeddings.

\section{Proposed Approach: MUSE}

This work proposes a framework to modularize two key mechanisms for multi-sense word representations: a \emph{sense selection module} and a \emph{sense representation module}.
The sense selection module decides which sense to use given a text context, whereas the sense representation module learns meaningful representations based on its statistical characteristics.
Unlike prior work that must suffer from either inefficient sense selection~\cite{Qiu2016ContextDependentSE} or coarse-grained representation learning~\cite{neelakantan2015efficient,li2015multi,bartunov2015breaking},
the proposed modularized framework is capable of performing efficient sense selection and learning representations in pure sense level simultaneously.

To learn sense-level representations, a sense selection model should be first established for sense identity decoding.
On the other hand, the sense embeddings should guide the sense selection model when decoding a sense identity sequence.
Therefore, these two modules should be tangled.
This indicates that a naive two-stage algorithm or two separate learning algorithms proposed by prior work are not optimal.

By connecting the proposed formulation with reinforcement learning literature, we design a novel joint training algorithm. Besides, taking advantage of the form of reinforcement learning, we are among the first to investigate various exploration techniques in sense selection for unsupervised sense embedding learning.

\subsection{Model Architecture}
Our model architecture is illustrated in Figure~\ref{fig:model}, where there are two modules in optimization.

\subsubsection{Sense Selection Module}
Formally speaking, given a corpus $C$, vocabulary $W$, and the $t$-th word $C_t=w_i \in W$, we would like to find the most probable sense $z_{ik} \in Z_i$, where $Z_i$ is the set of senses in word $w_i$.
Assuming that a word sense is determined by the local context, we exploit a local context $\bar{C_t} = \{C_{t-m},\cdots,C_{t+m}\}$ for sense selection according to the Markov assumption, where $m$ is the size of a context window.
Then we can either formulate a probabilistic policy $\pi(z_{ik} \mid \bar{C_t})$ about sense selection or estimate the \emph{individual} likelihood $q(z_{ik} \mid \bar{C_t})$ for each sense identity.

To ensure efficiency, here we exploit a linear neural architecture that takes word-level input tokens and outputs sense-level identities.
The architecture is similar to continuous bag-of-words (CBOW)~\cite{mikolov2013efficient}.
Specifically, given a \textit{word} embedding matrix $P$, the local context can be modeled as the summation of word embeddings from its context $\bar{C_t}$.
The output can be formulated with a 3-mode tensor $Q$, whose dimensions denote words, senses, and latent variables.
Then we can model $\pi(z_{ik} \mid \bar{C_t})$ or $q(z_{ik} \mid \bar{C_t})$ correspondingly.
Here we model $\pi(\cdot)$ as a \emph{categorical} distribution using a softmax layer:
\begin{equation}

    \pi(z_{ik} \mid \bar{C_t}) = \frac{\exp(Q_{ik}^T\sum_{j\in \bar{C_t}}P_j)}{\sum_{k'\in Z_i} \exp(Q_{ik'}^T\sum_{j\in \bar{C_t}}P_j)}.
    \label{eq:pg}

\end{equation}
On the other hand, the likelihood of selecting \emph{distinct} sense identities, $q(z_{ik}\mid \bar{C_t})$, is modeled as a \emph{Bernoulli} distribution with a sigmoid function $\sigma(\cdot)$:
\begin{equation}
    q(z_{ik} \mid \bar{C_t}) = \sigma( Q_{ik}^T\sum_{j \in \bar{C_t}}P_j ).
    \label{eq:q}

\end{equation}
Different modeling approaches require different learning methods,
especially for the unsupervised setting.
We leave the corresponding learning algorithms in \cref{sec:rl}.
Finally, with a built sense selection module, we can apply any selection algorithm such as a greedy selection strategy to infer the sense identity $z_{ik}$ given a word $w_i$ with its context $C_t$.

We note that modularized model enables efficient sense selection by leveraging word-level tokens, while remaining purely sense-level tokens in the representation module.
Specifically, if $n$ denotes $\max_k |Z_k|$, decoding $L$ words takes $O(nL)$ senses to be searched due to independent sense selection.
The prior work using a single model with purely sense-level tokens~\cite{Qiu2016ContextDependentSE} requires exponential time to calculate the collocation energy for every possible combination of sense identities within a context window, $O(n^{2m})$, for a \emph{single target sense}. Further, \newcite{Qiu2016ContextDependentSE} took an additional sequence decoding step with quadratic time complexity $O(n^{4m}L)$, based on an exponential number $n^{2m}$ in the base unit.
It demonstrates the achievement about sense inference efficiency in our proposed model.

\subsubsection{Sense Representation Module}

The semantic representation learning is typically formulated as a maximum likelihood estimation (MLE) problem for collocation likelihood.
In this paper, we use the skip-gram formulation~\cite{mikolov2013distributed} considering that it requires less training time,
where only two sense identities are required for stochastic training.
Other popular candidates, like GloVe~\cite{pennington2014glove} and CBOW~\cite{mikolov2013efficient}, require more sense identities to be selected as input and thus not suitable for our scenario.
For example, GloVe~\cite{pennington2014glove} takes computationally expensive collocation \emph{counting} statistics for each token in a corpus as input, which requires sense selection for every occurrence of the target word across the whole corpus for a single optimization step.

To learn the representations, we first create input sense representation matrix $U$ and collocation estimation matrix $V$ as the learning targets.
Given a target word $w_i$ and collocated word $w_j$ with corresponding local contexts, we map them to their sense identities as $z_{ik}$ and $z_{jl}$ by the sense selection module, and maximize the sense collocation log likelihood $\log \mathcal{L}(\cdot)$. A natural choice of the likelihood function is formulated as a categorical distribution over all possible collocated senses given the target sense $z_{ik}$:
\begin{equation}
\label{eq:categorical}
	 \max_{U,V}\;\;\log \mathcal{L}(z_{jl} \mid z_{ik}) = \log \frac{\exp (U_{z_{ik}}^TV_{z_{jl}})}{\sum_{z_{uv}}\exp(U_{z_{ik}}^TV_{z_{uv}})}.
\end{equation}
Instead of enumerating all possible collocated senses which is computationally expensive, we use the skip-gram objective (\ref{w2v})~\cite{mikolov2013distributed} to approximate (\ref{eq:categorical}) as shown in the green block of Figure~\ref{fig:model}.
\begin{align}
\label{w2v}
	 \max_{U,V}\;\;\log &\, \bar{\mathcal{L}}(z_{jl} \mid  z_{ik}) = \log \sigma(U_{z_{ik}}^TV_{z_{jl}}) \\
    & + \sum_{v=1}^M \mathbb{E}_{z_{uv}\sim p_{neg}(z)}[\log \sigma(-U_{z_{ik}}^T V_{z_{uv}})],\nonumber
    \vspace{-3pt}
\end{align}
where $p_{neg}(z)$ is the distribution over all senses for negative samples. In our experiment with $|Z_i|$ senses for word $w_i$, we use $(1/{|Z_i|})$ word-level unigram as sense-level unigram for efficiency and the $3/4$-th power trick in \citet{mikolov2013distributed}.

We note that our modular framework can easily maintain purely sense-level tokens with an arbitrary representation learning model.
In contrast, most related work using probabilistic modeling \cite{tian2014probabilistic,jauhar2015ontologically,li2015multi,bartunov2015breaking} binded sense representations with the sense selection mechanism, so efficient sense selection by leveraging word-level tokens can be achieved only at the cost of mixing word-level and sense-level tokens in their representation learning process.

\subsection{Learning}
\label{sec:rl}

Without the supervised signal for the proposed modules, it is desirable to connect two modules in a way where they can improve each other by their own estimations.
First, a trivial way is to forward the prediction of the sense selection module to the representation module.
Then we cast the estimated collocation likelihood as a reward signal for the selected sense for effective learning.

To realize the above procedure, we cast the learning problem a one-step Markov decision process (MDP) \cite{sutton1998reinforcement}, where the state, action, and reward correspond to context $\bar{C_t}$, sense $z_{ik}$, and collocation log likelihood $\log \bar{\mathcal{L}}(\cdot)$, respectively.
Based on different modeling methods ((\ref{eq:pg}) or (\ref{eq:q})) in the sense selection module, we connect the model to respective reinforcement learning algorithms to solve the MDP.
Specifically, we refer (\ref{eq:pg}) to policy distribution and refer (\ref{eq:q}) to Q-value estimation in the reinforcement learning literature.

The proposed MDP framework embodies several nuances of sense selection. First, the decision of a word sense is Markov: taking the whole corpus into consideration is not more helpful than a handful of necessary local contexts. Second, the decision making in MDP exploits a hard decision for selecting sense identity, which captures the sense selection process more naturally than a joint probability distribution among senses~\cite{Qiu2016ContextDependentSE}.
Finally, we exploit the reward mechanism in MDP to enable joint training: the estimation of sense representation is treated as a reward signal to guide sense selection. In contrast, the decision making under clustering~\cite{HuangEtAl2012,neelakantan2015efficient} considers the similarity within clusters instead of the outcome of a decision using a reward signal as MDP.

\subsubsection{Policy Gradient Method}
\label{sec:pg}

Because (\ref{eq:pg}) fits a valid probability distribution, an intuitive optimization target is the expectation of resulting collocation likelihood among each sense.
In addition, as the skip-gram formulation in (\ref{w2v}) is unidirectional ($\bar{\mathcal{L}}(z_{ik} \mid z_{jl}) \neq \bar{\mathcal{L}}(z_{jl} \mid z_{ik})$), we perform one-side optimization for the target sense $z_{ik}$ to stabilize model training\footnote{We observe about $4\%$ performance drop by optimizing input selection $z_{ik}$ and output selection $z_{jl}$ simultaneously.}.
That is, for the target word $w_i$ and the collocated word $w_j$ given respective contexts $\bar{C_t}$ and $\bar{C_{t'}}$ ($0<|t-t'|\leq m$), we first draw a sense $z_{jl}$ for $w_j$ from the policy $\pi(\cdot \mid \bar{C_{t'}})$ and optimize the expected collocation likelihood for the target sense $z_{ik}$ as follows,
\begin{equation}
\label{policy_grad}
\max_{P,Q}\;\; \mathbb{E}_{z_{ik} \sim \pi(\cdot \mid \bar{C_t})}[\log \bar{\mathcal{L}}(z_{jl} \mid z_{ik})].
\end{equation}
Note that (\ref{w2v}) can be merged into (\ref{policy_grad}) as a single objective.
The objective is differentiable and supports stochastic optimization \cite{lei2016rationalizing}, which uses a stochastic sample $z_{ik}$ for optimization.

However, there are two possible disadvantages in this formulation.
First, because the policy assumes the probability distribution in (\ref{eq:pg}), optimizing the selected sense must affect the estimation of the other senses.
Second, if applying stochastic gradient ascent to optimizing (\ref{policy_grad}), it would always lower the probability estimation for the selected sense $z_{ik}$ even if the model accurately selects the right sense.
The detailed proof is in Appendix \ref{doubly_sg}.

\subsubsection{Value-Based Method}
To address the above issues, we apply the Q-learning algorithm~\cite{mnih2013playing}.
Instead of maintaining a probabilistic policy for sense selection, Q-learning estimates the Q-value (resulting collocation log likelihood) for each sense candidate directly and independently.
Thus, the estimation of unselected senses may not be influenced by the selected one.
Note that in one-step MDP, the reward is equivalent to the Q-value, so we will use reward and Q-value interchangeably, hereinafter, based on the context.

We further follow the convention of recent neural reinforcement learning by reducing the reward range to aid model training~\cite{mnih2013playing}.
Specifically, we replace the \emph{log likelihood} $\log \bar{\mathcal{L}}(\cdot)\in (-\inf, 0]$ with the \emph{likelihood} $\bar{\mathcal{L}}(\cdot)\in [0,1]$ as the reward function.
Due to the monotonic operation in $\log()$, the relative ordering of the reward remains the same.

Furthermore, we exploit the probabilistic nature of likelihood for Q-learning.
To elaborate, as Q-learning is used to approximate the Q-value for each action in typical reinforcement learning, most literature adopted square loss to characterize the discrepancy between the target and estimated Q-values~\cite{mnih2013playing}.
In our setting where the Q-value/reward is a likelihood function, our model exploits cross-entropy loss to better capture the characteristics of probability distribution.

Given that the collocation likelihood in (\ref{w2v}) is an \emph{approximation} to the original categorical distribution with a softmax function shown in (\ref{eq:categorical}) \cite{mikolov2013distributed}, we revise the formulation by omitting the negative sampling term. The resulting formulation $\hat{\mathcal{L}}(\cdot)$ is a Bernoulli distribution indicating whether $z_{jl}$ collocates or not given $z_{ik}$:
\begin{equation}
\hat{\mathcal{L}}(z_{jl} \mid z_{ik}) = \sigma(U^T_{z_{ik}}V_{z_{jl}}).
\end{equation}
There are three advantages about using $\hat{\mathcal{L}}(\cdot)$ instead of approximated $\bar{\mathcal{L}}(\cdot)$ and original $\mathcal{L}(\cdot)$. First, regarding the variance of estimation, $\hat{\mathcal{L}}(\cdot)$ better captures $\mathcal{L}(\cdot)$ than $\bar{\mathcal{L}}(\cdot)$ because $\bar{\mathcal{L}}(\cdot)$ involves sampling:
\begin{equation}
Var(\bar{\mathcal{L}}(\cdot)) \geq Var(\hat{\mathcal{L}}(\cdot)) = Var(\mathcal{L}(\cdot)) = 0.
\end{equation}
Second, regarding the relative ordering of estimation, for any two collocated senses $z_{jl}$ and $z_{jl'}$ with a target sense $z_{ik}$, the following equivalence holds:
\begin{eqnarray}
&& \mathcal{L}(z_{jl} \mid z_{ik}) < \mathcal{L}(z_{jl'} \mid z_{ik})\\\nonumber
&\Leftrightarrow & \bar{\mathcal{L}}(z_{jl} \mid z_{ik}) < \bar{\mathcal{L}}(z_{jl'} \mid z_{ik})\\\nonumber
&\Leftrightarrow & \hat{\mathcal{L}}(z_{jl} \mid z_{ik}) < \hat{\mathcal{L}}(z_{jl'} \mid z_{ik})\nonumber
\end{eqnarray}
Third, for collocation computation, $\mathcal{L}(\cdot)$ requires all sense identities and $\bar{\mathcal{L}}(\cdot)$ requires $(M+1)$ sense identities, whereas $\hat{\mathcal{L}}(\cdot)$ only requires $1$ sense identity.
In sum, the proposed $\hat{\mathcal{L}}(\cdot)$ approximates $\mathcal{L}(\cdot)$ with no variance, no ``bias'' (in terms of relative ordering), and significantly less computation.

Finally, because both target distribution $\hat{\mathcal{L}}(\cdot)$ and estimated distribution $q(\cdot)$ in (\ref{eq:q}) are Bernoulli distributions, we follow the last section to conduct one-side optimization by fixing a collocated sense $z_{jl}$ and optimize the selected sense $z_{ik}$ with cross entropy as
\begin{equation}
\label{Qlearn}
 \min_{P,Q} \;\; H(\hat{\mathcal{L}}(z_{ik}\mid z_{jl}), q(z_{ik}\mid \bar{C_{t}}))
\end{equation}

\subsubsection{Joint Training}

To jointly train sense selection and sense representation modules, we first select a pair of the collocated senses, $z_{ik}$ and $z_{jl}$, based on the sense selection module with any selecting strategy (e.g. greedy), and then optimize the sense representation module and the sense selection module using the above derivations. Algorithm~\ref{algo} describes the proposed MUSE model training procedure.

As modular frameworks, the major distinction between our modular framework and two-stage clustering-representation learning framework \cite{neelakantan2015efficient,vu2016k} is that we establish a reward signal from the sense representation to the sense selection module to enable immediate and joint optimization.

\begin{algorithm}[t]
\caption{Learning Algorithm}
\label{algo}
 \For{$w_i=C_t\in C$}{
   sample $w_j=C_{t'} (0<|t'-t|\leq m$)\;
   $z_{ik}=$ select$({C_t}, w_i)$\;
   $z_{jl}=$ select$({C_{t'}}, w_j)$\;
   optimize $U,V$ by (\ref{w2v}) for the sense representation module\;
   optimize $P,Q$ by (\ref{policy_grad}) or (\ref{Qlearn}) for the sense selection module\;
 }
\end{algorithm}

\subsection{Sense Selection Strategy}

Given a fitness estimation for each sense, exploiting the greedy sense is the most popular strategy for clustering algorithms \cite{neelakantan2015efficient, kaageback2015neural} and hard-EM algorithms \cite{Qiu2016ContextDependentSE, jauhar2015ontologically} in literature. However, there are two incentives to conduct exploration. First, in the early training stage when the fitness is not well estimated, it is desirable to explore underestimated senses. Second, due to high ambiguity in natural language, sometimes multiple senses in a word would fit in the same context. The dilemma between exploring sub-optimal choices and exploiting the optimal choice is called exploration-exploitation trade-off in reinforcement learning \cite{sutton1998reinforcement}.

We introduce exploration mechanisms for sense selection for both policy gradient and Q-learning.
For policy gradient, we sample the policy distribution to approximate the expectation in (\ref{policy_grad}).
Because of the flexible formulation of Q-learning, the following classic exploration mechanisms are applied to sense selection:
\begin{compactitem}
  \item \emph{Greedy}: selects the sense with the largest Q-value (no exploration).
  \item \emph{$\epsilon$-Greedy}: selects a random sense with $\epsilon$ probability, and adopts the greedy strategy otherwise \cite{mnih2013playing}.
  \item \emph{Boltzmann}: samples the sense based on the Boltzmann distribution modeled by Q-value. We directly use (\ref{eq:pg}) as the Boltzmann distribution for simplicity.
\end{compactitem}
We note that Q-learning with Boltzmann sampling yields the same sampling process as policy gradient but different optimization objectives.
To our best knowledge, we are among the first to explore several exploration strategies for unsupervised sense embedding learning.

In the following sections, MUSE-Policy denotes the proposed MUSE model with policy learning and MUSE-Greedy denotes the model using corresponding sense selection strategy for Q-learning.

\section{Experiments}

We evaluate our proposed MUSE model in both quantitative and qualitative experiments.

\subsection{Experimental Setup}
Our model is trained on the April 2010 Wikipedia dump~\cite{wikidump}, which contains approximately 1 billion tokens.
For fair comparison, we adopt the same vocabulary set as \citet{HuangEtAl2012} and \citet{neelakantan2015efficient}.
For preprocessing, we convert all words to their lower cases, apply the Stanford tokenizer and the Stanford sentence tokenizer \cite{manning-EtAl:2014:P14-5}, and remove all sentences with less than 10 tokens.
The number of senses per word in $Q$ is set to 3 as the prior work~\cite{neelakantan2015efficient}.

In the experiments, the context window size is set to 5 ($|\bar{C_t}|=11$).
Subsampling technique introduced by \texttt{word2vec} \cite{mikolov2013distributed} is applied to accelerate the training process.
The learning rate is set to 0.025.
The embedding dimension is 300.
We initialize $Q$ and $V$ as zeros, and $P$ and $U$ from uniform distribution $[-\sqrt{1/100}, \sqrt{1/100}]$ such that each embedding has unit length in expectation \cite{lei2015molding}.
Our model uses 25 negative senses for negative sampling in (\ref{w2v}). We use $\epsilon = 5\%$ for $\epsilon$-Greedy sense selection strategy

In optimization, we conduct mini-batch training with 2048 batch size using the following procedure: 1) select senses in the batch; 2) optimize $U,V$ using stochastic training within the batch for efficiency; 3) optimize $P,Q$ using mini-batch training for robustness.

\subsection{Experiment 1: Contextual Word Similarity}

To evaluate the quality of the learned sense embeddings, we compute the similarity score between each word pair given their respective local contexts and compare with the human-judged score using Stanford's Contextual Word Similarities (SCWS) dataset \cite{HuangEtAl2012}.
Specifically, given a list of word pairs with corresponding contexts, $S = \{(w_i, \bar{C_t}, w_j, \bar{C_{t'}})\}$, we calculate the Spearman's rank correlation $\rho$ between human-judged similarity and model similarity estimations\footnote{For example, human-judged similarity between ``... east \textbf{bank} of the Des Moines River ...'' and ``... basis of all \textbf{money} laundering ...'' is 2.5 out of 10.0 in SCWS dataset~\cite{HuangEtAl2012}.}.
Two major contextual similarity estimations are introduced by \citet{reisinger2010multi}: AvgSimC and MaxSimC.
AvgSimC is a \emph{soft} measurement that addresses the contextual information with a probability estimation:
\begin{align}
\text{AvgSimC} & (w_i, \bar{C_t}, w_j, \bar{C_{t'}}) = \nonumber\\
\sum_{k=1}^{|Z_i|} & \sum_{l=1}^{|Z_j|} \pi(z_{ik}|\bar{C_t}) \pi(z_{jl}|\bar{C_{t'}}) d(z_{ik}, z_{jl}),\nonumber
\end{align}
where $d(z_{ik}, z_{jl})$ refers to the cosine similarity between $U_{z_{ik}}$ and $U_{z_{jl}}$.
AvgSimC weights the similarity measurement of each sense pair $z_{ik}$ and $z_{jl}$ by their probability estimations.
On the other hand, MaxSimC is a \emph{hard} measurement that only considers the most probable senses:
\begin{align*}
\text{MaxSimC}&(w_i, \bar{C_t}, w_j, \bar{C_{t'}}) = d(z_{ik}, z_{jl}),\\
z_{ik}=&\argmax_{z_{ik'}}\pi(z_{ik'}|\bar{C_t}),\\
z_{jl}=&\argmax_{z_{jl'}} \pi(z_{jl'}|\bar{C_{t'}}).
\end{align*}

\begin{table}
\centering
\begin{tabular}{l c c c}
\hline
\bf Method & \bf \small{MaxSimC} & \bf \small{AvgSimC}\\
\hline\hline
\newcite{HuangEtAl2012} & 26.1 & 65.7\\
\newcite{neelakantan2015efficient} & 60.1 & \bf 69.3\\
\newcite{tian2014probabilistic} & 63.6 & 65.4\\
\newcite{li2015multi} & 66.6 & 66.8\\
\newcite{bartunov2015breaking} & 53.8 & 61.2 \\
\newcite{Qiu2016ContextDependentSE} & 64.9 & 66.1\\
\hline
MUSE-Policy &  66.1 & 67.4\\
MUSE-Greedy & 66.3 & 68.3\\
MUSE-$\epsilon$-Greedy & 67.4$^\dag$ & 68.6\\
MUSE-Boltzmann & \bf 67.9$^\dag$ & 68.7\\
\hline
\end{tabular}
\vspace{-2mm}
\caption{Spearman's rank correlation $\rho$ x100 on the SCWS dataset. $^\dag$ denotes superior performance to all unsupervised competitors.}
\label{tab:scws}
\end{table}

The baselines for comparison include
classic clustering methods \cite{HuangEtAl2012,neelakantan2015efficient}, EM algorithms \cite{tian2014probabilistic,Qiu2016ContextDependentSE,bartunov2015breaking}, and Chinese Restaurant Process \cite{li2015multi}\footnote{We run \citet{li2015multi}'s released code on our corpus for fair comparison.},
where all approaches are trained on the same corpus except \citet{Qiu2016ContextDependentSE} used more recent Wikipedia dumps.
The embedding sizes of all baselines are 300, except 50 in \citet{HuangEtAl2012}.
For every competitor with multiple settings, we report the best performance in each similarity measurement setting and show in Table~\ref{tab:scws}.

\begin{table}[t]
\centering
\begin{tabular}{ l  c  c  c }
\hline
\bf Method & \bf\small ESL-50 & \small \bf RD-300 & \small \bf TOEFL-80\\
\hline\hline
\multicolumn{4}{l}{\textit{1) Conventional Word Embedding}}\\
Global Context & 47.73 & 45.07 & 60.87\\
Skip-Gram & 52.08 & 55.66 & 66.67\\
\hline
\multicolumn{4}{l}{\textit{2) Word Sense Disambiguation}}\\
IMS+SG & 41.67 & 53.77 & 66.67\\
\hline
\multicolumn{4}{l}{\textit{3) Unsupervised Sense Embeddings}}\\
EM & 27.08 & 33.96 & 40.00\\
MSSG & 57.14 & 58.93 & 78.26\\
CRP & 50.00 & 55.36 & 82.61\\
\hline
MUSE-\small{Policy} & 52.38 & 51.79 & 79.71\\
MUSE-\small{Greedy} & 57.14 & 58.93 & 79.71\\
MUSE-\small{$\epsilon$-Greedy} & 61.90$^\dag$ & 62.50$^\dag$ & 84.06$^\dag$\\
MUSE-\small{Boltzmann} & \bf 64.29$^\dag$ & \bf 66.07$^\dag$ & \bf 88.41$^\dag$\\
\hline\hline
\multicolumn{4}{l}{\textit{4) Supervised Sense Embeddings}}\\
Retro-GC & 63.64 & 66.20 & 71.01\\
Retro-SG & 56.25 & 65.09 & 73.33\\
\hline
\end{tabular}
\vspace{-2mm}
\caption{Accuracy on synonym selection. $^\dag$ denotes superior performance to all unsupervised competitors.}
\label{tab:synonym}
\end{table}

Our MUSE model achieves the state-of-the-art performance on MaxSimC, demonstrating superior quality on independent sense embeddings.
On the other hand, MUSE achieves comparable performance with the best competitor in terms of AvgSimC (68.7 vs. 69.3), while MUSE outperforms the same competitor significantly in terms of MaxSimC (67.9 vs. 60.1).
The results demonstrate not only the high quality of sense representations but also accurate sense selection.

From the application perspective, MaxSimC refers to a typical scenario using single embedding per word, while AvgSimC employs multiple sense vectors simultaneously per word, which not only brings computational overhead but changes existing neural architecture for NLP.
Hence, we argue that MaxSimC better characterize practical usage of a sense representation system than AvgSimC.

Among various learning methods for MUSE, policy gradient performs worst, echoing our argument in \cref{sec:pg}. On the other hand, the superior performance of Boltzmann sampling and $\epsilon$-Greedy over Greedy selection demonstrates the effectiveness of exploration.

Finally, replacing $\bar{\mathcal{L}}(\cdot)$ with $\hat{\mathcal{L}}(\cdot)$ as the reward signal yields $2.3$ times speedup for MUSE-$\epsilon$-Greedy and $1.3$ times speedup for MUSE-Boltzmann to reach 67.0 in MaxSimC, which demonstrates the efficacy of proposed approximation $\hat{\mathcal{L}}(\cdot)$ over typical $\bar{\mathcal{L}}(\cdot)$ in terms of convergence.

\subsection{Experiment 2: Synonym Selection}

We further evaluate our model on synonym selection using multi-sense word representations~\cite{jauhar2015ontologically}.
Three standard synonym selection datasets, ESL-50 \cite{turney2001mining}, RD-300 \cite{jarmasz2004roget}, and TOEFL-80 \cite{landauer1997solution}, are performed.
In the datasets, each question consists of a question word $w_Q$ and four answer candidates $\{w_A, w_B, w_C, w_D\}$, and the goal is to select the most semantically synonymous choice among the four candidates.
For example, in the TOEFL-80 dataset, a question shows $\{$(Q) enormously, (A) appropriately, (B) uniquely, (C) tremendously, (D) decidedly$\}$, and the answer is (C).
For multi-sense representations system, it selects the synonym of the question word $w_Q$ using the maximum sense-level cosine similarity as a proxy of the semantic similarity \cite{jauhar2015ontologically}.

Our model is compared with the following baselines:
1) conventional word embeddings: global context vectors \cite{HuangEtAl2012} and skip-gram \cite{mikolov2013distributed};
2) applying supervised word sense disambiguation using the IMS system  and then applying skip-gram on disambiguated corpus (IMS+SG)~\cite{zhong2010makes};
3) unsupervised sense embeddings: EM algorithm \cite{jauhar2015ontologically}, multi-sense skip-gram (MSSG) \cite{neelakantan2015efficient}, Chinese restaurant process (CRP) \cite{li2015multi}, and the MUSE models;
4) supervised sense embeddings with WordNet~\cite{miller1995wordnet}:
retrofitting global context vectors (Retro-GC) and retrofitting skip-gram (Retro-SG) \cite{jauhar2015ontologically}.

Among unsupervised sense embedding approaches, CRP and MSSG refer to the baselines with highest MaxSimC and AvgSimC in Table~\ref{tab:scws} respectively.
Here we report the setting for baselines based on the best average performance in this task.
We also show the performance of supervised sense embeddings as an upperbound of unsupervised methods
due to the usage of additional supervised information from WordNet.

\begin{table*}[t]
\centering
\begin{tabular}{l l }
\hline
\bf Context & \bf k-NN Senses\\
\hline\hline
\small $\cdots$ braves finish the season in \textbf{tie} with the los angeles dodgers $\cdots$ & \small scoreless otl shootout 6-6 hingis 3-3 7-7 0-0\\
\small $\cdots$ his later years proudly wore \textbf{tie} with the chinese characters for $\cdots$ & \small pants trousers shirt juventus blazer socks anfield \\
\hline
\small $\cdots$ of the mulberry or the \textbf{blackberry} and minos sent him to $\cdots$ & \small cranberries maple vaccinium apricot apple \\%  blackberries blueberry milk prunus\\
\small $\cdots$ of the large number of \textbf{blackberry} users in the us federal $\cdots$ & \small smartphones sap microsoft ipv6 smartphone\\% linux-based pda messaging symbian\\
\hline
\small $\cdots$ shells and/or high explosive squash \textbf{head} hesh and/or anti-tank $\cdots$ & \small venter thorax neck spear millimeters fusiform \\% beachy head shaved maldives whale\\
\small $\cdots$ head was shaven to prevent \textbf{head} lice serious threat back then $\cdots$ & \small shaved thatcher loki thorax mao luthor chest \\% pressure
\small $\cdots$ appoint john pope republican as \textbf{head} of the new army of $\cdots$ & \small multi-party appoints unicameral beria appointed \\
\hline
\end{tabular}
\vspace{-2mm}
\caption
Different word senses are selected by MUSE according to different contexts. The respective k-NN (sorted by collocation likelihood) senses are shown to indicate respective semantic meanings.}
\label{tab:qual}
\end{table*}

The results are shown in Table~\ref{tab:synonym}, where our MUSE-$\epsilon$-Greedy and MUSE-Boltzmann significantly outperform all unsupervised sense embeddings methods, echoing the superior quality of our sense vectors in last section.
MUSE-Boltzmann also outperforms the supervised sense embeddings except 1 setting without any supervised signal during training.
Finally, the MUSE methods with proper exploration outperform all unsupervised baselines consistently, demonstrating the importance of exploration.

\subsection{Qualitative Analysis}

We further conduct qualitative analysis to check the semantic meanings of different senses learned by MUSE with k-nearest neighbors (k-NN) using sense representations.
In addition, we provide contexts in the training corpus where the sense will be selected to validate the sense selection module.
Table \ref{tab:qual} shows the results.
The learned sense embeddings of the words ``tie'', ``blackberry'', and ``head'' clearly correspond to correct senses under different contexts.

Since we address an unsupervised setting that learns sense embeddings from unannotated corpus, the discovered senses highly depend on the training corpus. From our manual inspection, it is common for our model to discover only two senses in a word, like "tie" and "blackberry". However, we maintain our effort in developing unsupervised sense embeddings learning methods in this work, and the number of discovered sense is not a focus.

\section{Conclusion}

This paper proposes a novel modularized framework for unsupervised sense representation learning, which supports not only the flexible design of modular tasks but also joint optimization among modules.
The proposed model is the first work that implements purely sense-level representation learning with linear-time sense selection, and
achieves the state-of-the-art performance on benchmark contextual word similarity and synonym selection tasks.
In the future, we plan to investigate reinforcement learning methods to incorporate multi-sense word representations for downstream NLP tasks.

\section*{Acknowledgements}
We would like to thank reviewers for their insightful comments on the paper.
The authors are supported by the Ministry of Science and Technology of Taiwan under the contract number 105-2218-E-002-033, Institute for Information Industry, and MediaTek Inc..

redpajama_set_name: RedPajamaArXiv


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National Securities Depository Limited (NSDL) is an Indian central securities depository based in Mumbai.NSDL provides unique PAN card facilities to all Indian and it is Authorised by Govt. of India.You can check your PAN card status at pancardstatusonline.co.in.If you applied for your PAN card through NSDL then you can track your PAN card status from here.
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A PAN applicant who has 15 digits acknowledgement number can track PAN card status through the Online process.
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text:
Q: Python - Selenium: How to click on the element with text as "Year" within the webpage from bs4 import BeautifulSoup as soup
from openpyxl import load_workbook
from openpyxl.styles import PatternFill, Font
from selenium import webdriver
from selenium.webdriver.common.by import By
import datetime
import os
import time
import re

browser = webdriver.Chrome(executable_path=r"C:\Program Files(x86)\Google\Chrome\Application\chromedriver.exe")
my_url = 'https://www.eex.com/en/market-data/power/futures/phelix-at-futures#!/2017/07/24'
browser.get(my_url)
button = browser.find_elements_by_class_name('ng-scope')[-1]
browser.execute_script("arguments[0].click();", button)

It does not give me any error but it is not actually clicking. It should change from month to year, since it is the last element, but it stays there.

A: As per the url you have provided to click on the element with text as Year you have to induce WebDriverWait for the element to be clickable as follows:

*

*CSS_SELECTOR:
WebDriverWait(driver, 20).until(EC.element_to_be_clickable((By.CSS_SELECTOR, "ul.tabs.filter_wrap.clearfix li.ng-scope:nth-child(3)>a"))).click()

*XPATH:
WebDriverWait(driver, 20).until(EC.element_to_be_clickable((By.XPATH, "//ul[@class='tabs filter_wrap clearfix']//li[@class='ng-scope']/a[contains(.,'Year')]"))).click()

A: Try this:
from bs4 import BeautifulSoup as soup
from openpyxl import load_workbook
from openpyxl.styles import PatternFill, Font
from selenium import webdriver
from selenium.webdriver.common.by import By
import datetime
import os
import time
import re

browser = webdriver.Chrome(executable_path=r"C:\Program Files(x86)\Google\Chrome\Application\chromedriver.exe")
my_url = 'https://www.eex.com/en/market-data/power/futures/phelix-at-futures#!/2017/07/24'
browser.get(my_url)
# wait until button will be present
WebDriverWait(driver, 120).until(EC.presence_of_element_located((By.XPATH, "//*[@id='content']/div/div/ul/li[3]/a")))
# find the button and click on it
button = driver.find_element_by_xpath("//*[@id='content']/div/div/ul/li[3]/a")
button.click()

A: You can peel the same apple in different ways. However, in this very case the better approach would be to use .find_element_by_link_text() which is less likely to break. Give it a shot:
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC

url = 'https://www.eex.com/en/market-data/power/futures/phelix-at-futures#!/2017/07/24'

driver = webdriver.Chrome()
driver.get(url)
wait = WebDriverWait(driver, 30)

wait.until(EC.presence_of_element_located((By.LINK_TEXT, "Year"))).click()
driver.quit()

redpajama_set_name: RedPajamaStackExchange


text:
Dirt Trackers Kart Club (DTKC) was formed after an advert was placed in the West Australian Newspaper and club founder Ken Ditchfield Snr called the inaugural meeting. Approximately 50 people attended the meeting and that night they formed the first committee of the Dirt Trackers Kart Club. A Busy Bee was organised at Ken Ditchfield Senior's gravel pit at 26 Mile Peg Road, Gidgegannup. The track was pegged out, constructed and formed in four weeks.
The first race meeting was held with 14 karts. The tower was just a tin shed and was operated by the late Fred Holmes, stepfather of Ted Gittos. Rose Lawrence ran the canteen from the back of a Ute. The club raced at Gidgegannup for approximately 2 seasons, then Ken Ditchfield Snr had the foresight to move the Dirt Trackers Kart Club to the infamous Kewdale site. Here, the club ran very successfully and hosted several national titles. Unfortunately due to industrial development the Dirt Trackers Kart Club was once again on the move.
They have been at the current location at Boomerang Road, Oldbury for more than 22 years.
Membership has grown to approximately 200 racing members and 80 social members since 1992.
The original Dirt Trackers Kart Club was run under Australian Karting Association (AKA) governing body. In line with development, DTKC changed to the Australian Independent Dirt Kart Association (AIDKA) banner in 1992. DTKC has held both the State and National Championships as recently as 2010. The club is proud of these achievements considering its humble beginnings with 14 racing members.
Dirt Kart racing is the answer to keeping Karting at an affordable level. We cater for people of all ages, ranging between the ages of 5 and 70 years. Worldwide, many racing drivers from speedway to F1 have developed their skills Karting on both dirt and bitumen. Some of the names you may recognise are listed below:
Craig Lowndes
Jamie Whincup
Ryan Farrel
Shane Krikke
John Fenton
Marcus Ambrose
Geoff Pilgrim

redpajama_set_name: RedPajamaCommonCrawl


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Case is made of a light felt on the outside with contrast layer of felt inside with pocket in the front.


5 Keys to Measuring Effective Sales Management - Activate Group Inc.
Are sales managers effective? How would one know if they could and should be getting more performance out of the sales team? How do you know if money is being left on the table?
Leading Indicators – Do your sales manager hold people to leading indicators? Once produced will sales results occur with certainty? Are people being held accountable to these indicators?
Time Management – Are managers spending the proper amount of time coaching, motivating, holding accountable, and recruiting salespeople when compared against the "ideal" manager?
Skills – Are manager skills in the areas performance management, mentoring, coaching, motivating, and recruiting comparable to those of the "ideal" manager?
Effectiveness – How effective are managers in terms of impacting our sales force when it comes to holding accountable, motivating, mentoring, coaching, growing, and recruiting sales people?
Top Grading – Healthy turnover in a sales force is between 20% and 30%. A higher percentage usually indicates a recruiting and/or management problem. A lower percentage usually means management has set too low a standard for the sales force. The company should always be recruiting salespeople and pruning the lowest performers.
There are some great tools you can use to assess your sales management objectively. Contact us if you want help!


The mobile gaming company Gameloft has appointed a new country manager for the UK and Ireland.
John-Paul Burke has joined the team as Gameloft continues its plans for growth both in the UK and Ireland.
In his new role, Burke will oversee the sales and marketing teams as well as lead Gameloft Advertising Solutions, the advertising arm that aims to help brands and agencies engage with more than three million UK players.
The new country manager has previously enjoyed stints as head of AV at Arena Azure, Dennis Publishing and spent five years at Bin Weevils as head of sales.
Antonin Lhuillier, managing director, North Europe, Gameloft, said: "We are entering a hugely exciting period of growth across the global business and John-Paul will play a key role. His combination of expertise and passion for the gaming and entertainment industries made the decision an easy one.
Gameloft is the developer behind a number of hit mobile game titles, including Marvel's Spider-Man Unlimited, Ice Age Adventures and Disney's Magic Kingdoms.


text:
Feb-Taxpayer Advocacy Group Announces
Americans for Tax Reform (ATR) creates new caucus, organized by Reps Ray Haynes and John Campbell, for Calif. legislators who believe in taxpayers\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' rights and are committed to stop tax hikes.
WASHINGTON – Today, Americans for Tax Reform (ATR), America\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'s leading taxpayer advocacy organization, announced the formation of the "Taxpayer Protection Caucus" for the California legislature. The caucus will be headed by Reps John Campbell of District 70 and Ray Haynes of District 66.
The creation of the caucus comes as no surprise, as state after state around the nation have fallen victim to overspending in their capitals, which has threatened taxpayers with potential tax increases. Currently, 44 states around the nation are running these potential deficits that threaten taxpayers. California is facing a shortfall that dwarfs every other state.
"Protecting taxpayers should be the first priority of any legislator," said taxpayer advocate Grover Norquist, who heads ATR in Washington. "Reps Campbell and Haynes have shown bold leadership in fighting against tax hikes in California, and taxpayers all across the state owe them debts of gratitude," he continued.
To join the caucus, legislators must sign and keep ATR\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'s Taxpayer Protection Pledge, which is a written promise to their constituents to "oppose any and all efforts to increase taxes." Every state (with the exception of Nebraska) will have both a Senate and House Taxpayer Protection Caucus, consisting of all pledge signers in the legislature.
The Caucus will seek to provide a single voice on tax issues among pro-taxpayer legislators. For many, it is difficult to be a stand-alone Pledge signer. The caucus will create an entire body of legislators that believe in the same principle – no new taxes. Considering the budget battles all states will face in the upcoming year, taxes will be very high on the radar screen of the media. Journalists will be able to go to the chair or vice-chair of the caucus for comments on tax issues.
"It is tremendously important that pro-taxpayer leaders in statehouses across the nation are on the same page, not only in terms of rhetoric, but policy as well," continued Norquist. "The best thing is that unless they want to be driven into electoral oblivion, legislative tax-hikers will be left knee-deep in a quagmire. What will they call themselves? The Tax-Me-More-To-Fund-Pork caucus?"


text:
Батово — деревня на севере Бежаницкого района Псковской области. Входит в состав сельского поселения муниципальное образование «Добрывичская волость».

Расположена на берегу реки Самородовка, в 31 км к северу от райцентра Бежаницы и в 1 км к востоку от деревни Шилово.

Население
Численность населения деревни по состоянию на 2000 год составила 17 человек.

История
До 2005 года деревня входида в состав ныне упразднённой Шиловской волости.

Примечания

Населённые пункты Бежаницкого района

redpajama_set_name: RedPajamaWikipedia


Are you interested in discovering the secret to dramatically improving and transforming your life faster than you ever imagined possible? If you are, this is the book for you!
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Does the state of our health always have to begin with weight, diet, and exercise? What about well-being; our state of mind and level of satisfaction with life. Doesn't that influence how happy or depressed we feel, the food choices we make, and our level of motivation to get to the gym. What if we shifted our perspective and started to consider our mental, physical, and social well-being? Might that approach to improving our health naturally allow weight management, diet and exercise to naturally fall into place? Let's explore.
At some point in our lives we will all take a moment to evaluate the state of our health and wellness. First we get on the scale and choose some abstract number of pounds we think we need to lose. We then move to the refrigerator to begin evaluating the foods we need to start and/or stop eating. All of a sudden we begin to feel discouraged realizing we are about to lose all the things we love and enjoy. Finally, our mind shifts to exercise, oh the drudgery of going to the gym. We are suddenly filled with excuses. Our feeling of anxiety, intimidation, and overwhelm are so strong we get a pit in our stomachs.
Why do we focus on these three areas first? Why aren't we considering the real factors contributing to weight gain, bad food choices, and lack of motivation to go to the gym. Why are we always so quick to look outside for the answers when we all know the real answer lie within? Why do we fear going to the place that will bring us real long lasting results once and for all?
When we begin to consider our health [being without illness or injury] and wellness [condition of good physical and mental health] we prefer to focus on the BIG three because it is safe and seems easier. Going within can be scary and a lot of work; who has the time to figure out their real issues. I would like to suggest you consider taking a risk, the reward could be big. Ask yourself some seriously tough questions and be honest with yourself. Are you comfortable with yourself? Are your fit and able to engage in activities you enjoy? Are you happy, content, joyful. If you have answered no or not sure, you have some thinking to do.
Your feeling of being comfortable, fit, and happy is about your state of well-being. Well-being is a primary contributing factor to our state of health. We can continue trying to losing that last 20lbs, eating the foods we know are healthy and getting to the gym regularly, but if we don't have a positive sense of well-being we will often land right back where we started.
Well-being encompasses our physical, mental, and social aspects of life. Your physical well-being relates to your feeling of energy, vitality, and vigor for life. Mental well-being includes your sense of satisfaction for life, acceptance of self; recognizing our purpose and passion; experiencing balance between positive and negative emotions; feeling in control of life circumstances and seeking out personal growth and development opportunities. Finally, social well-being, when you feel as though you give and receive support from friends, family and colleagues. People who feel a greater sense of well-being will rate their lives as positive and will feel more optimistic, ultimately being healthier.
Do you currently feel healthy, full of energy and able to take on your daily activities? Are you engaged in your life? Do you find it interesting and satisfying? Are you surrounded by supportive friends, family? People with a sense of well-being evaluate their lives as meaningful and having purpose. Does this describe you? Maybe this is where you need start in the evaluation of your state of health. Stop looking at the scale, eating more broccoli, and avoiding the gym. Take the time, energy, and risk; go deeper to figure what is really effecting your state of health.


A political impasse between Republicans and Democrats that led to a U.S. government shutdown in recent days could have devastating consequences for the global economy if it translates into a debt default, the head of JPMorgan Chase & Co's Brazilian unit said on Tuesday.
The impasse is probably going to be resolved at the very last minute, Jose Berenguer, JPMorgan's senior country officer in Brazil, said at an event sponsored by Brazil's banking lobby group Febraban.
If the United States defaulted, it would be just a for very short time, he added.


text:
Pine Street Inn, hopeFound Explore Merger
August 17, 2011 By David J. Jefferson
Adam Sennott
Two of Boston's leading homeless shelters announced August 5th that they are considering a merger that they believe could allow them to have a greater impact in Boston's fight to end homelessness.
Pine Street Inn, New England's largest homeless shelter, and hopeFound, formerly known as Friends of the Shattuck Shelter, announced August 5th that they are moving forward with talks about a possible merger.
According to Mary Nee, Executive Director of hopeFound, The board of directors for both hopeFound and Pine Street Inn recently gave approval to continue exploring the benefits of a potential merger, Nee also stressed the talks were focused around strengthening services and are not being looked at for cost saving reasons.
According to Nee, hopeFound initially approached Pine Street Inn about a potential merger because Pine Street offered many of the services hopeFound had been looking to expand upon, such as housing and job training.
"hopeFound began the conversations," said Nee. "It began because we were doing strategic planning for the future, and we were thinking about what were the kinds of critical services we needed to help us advance our mission of ending homelessness. When we looked at what we offered and what we needed; the needs that came about were access to more housing and really, job training.
"We asked ourselves the question, do we as an agency grow these service; begin to build housing, manage housing, develop job training programs or would we be better served partnering with another organization? It was really through that thought process that we look in our field and saw that Pine Street was an agency who we served the same populations, we share missions and philosophy's around ending homelessness and moving people to housing and we found that we had a number of very complementary programs"
One set of programs in particular that complemented each other were Pine Street Inn's job training program and hopeFound's job placement program.
"Pine Street has a very strong job training program and hopeFound has a very strong job placement program, and so by aligning those two we hope that we could be even better able to serve," Jennifer Harris, a Spokesperson for Pine Street Inn said. "This year we had nearly 100 people graduate from our three job training programs."
Added Nee, "We have a large addiction treatment program, that's not something that they're significant in, we do a lot of job placement, but they do a lot of job training, and obviously they are one of the largest managers and developers of affordable housing for homeless individuals and that is a marvelous resource that we would like to have available to the people we serve.
According to Nee, both Pine Street Inn and hopeFound had already established a working relationship with one another before talks of a merger even commenced. Because of this relationship it made broaching the subject of a potential merger that much easier.
"Both agencies have a great deal of respect for one another and for our work, so that made it easy," said Nee.
"We've worked on a number of joint projects, we're part of a city convened group that's looking at long term shelter stayers and how we can access housing for those folks, our street outreach teams collaborate on a day to day basis, we have operated for the past 17 years on impact employment services and one of our largest users of those services in fact are men and women who are living at Pine Street."
Although cuts to public funding could affect service providers across the state, Nee stressed that the potential merger had more to do with strengthening programs and services that help the homeless then dealing with potential cuts in funding.
"We did not begin these discussions because we we're trying to have cost savings. This was not about let's be more efficient," said Nee. "These discussions were all about, let's have more impact."
Nee also said that the discussion with Pine Street Inn would continue over the next three months.
"We began a conversation to say does this make sense, and at a very high level, our boards, think that there's a lot of potential. So, what we've decided to do is to really have a more public discussion with our staff, our funders and our clients and really examine what would it take, would be the obstacles, what would be the benefits of coming together, and that's what we said we're going to do over the next three or four months."
Tags Hopefound Merger, Pine Street Inn
Healthcare Advocates Gather in Remembrance for Overdose Awareness


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Italian Producers Could Lose $200M if U.S. Tariffs Approved, Trade Group Warns
Use of Dimethoate to be Banned in EU
The Free Weekly Newsletter from Olive Oil Times
Trials and Tribulations of Commercial Olive Oil Production in the U.K.
By Jacki Topiol on January 27, 2012
Filed in Making Olive Oil
The United Kingdom might not be the first location for a commercial olive grove that springs to mind, but Neil Davy, proprietor of Huggit's Farm in Kent, has other ideas.
Kent, a county in the south of England, has one of the warmest and driest climates in the country. And this is one of the reasons Mr. Davy is confident that olive trees might just have a better than average chance of thriving in an area that's known as 'The Garden of England.'
The trees chosen are a mixture of both self-fertile and pollinators, with the idea that this will help to increase the amount of oil yielded from the fruits. All have been specifically planted because of their resilience to the cold, as well as other considerations such as oil content, distinct flavors and resistance to disease. The varieties of olive trees include Frantoio, Picholine and Pendolino.
Snow and Gales
Huggit's Farm's olive tree venture began in 2010. After some initial test planting of trees, the winter of 2010/11 proved to be unseasonably harsh, with snow blanketing the countryside. This was followed by a dry and warm summer, and this winter has, so far, been extremely mild.
However, the biggest concern is the stress caused to the trees by wind. The site is located next to ancient sea cliffs and already this winter has been battered by winds of up to 60 mph.
But, as Mr. Davy says, "Although wanting all our trees to thrive, we are really hoping that as we conduct our trials, Mother Nature delivers the worst she can muster to put the trees through their paces."
"Of the six cultivars we have planted, a couple of trees do look to be struggling, some Leccino and Frantoios particularly. However, other trees of the same cultivar are thriving, so we draw some consolation that it may be those individual trees, rather than the variety, that could struggle."
Of course, only time will tell if the trees will manage to thrive here. Climate change is very real, and the UK is growing crops that once would never have been considered a possibility. So if the experiment goes to plan, it appears that olive groves, once a sight only seen in Mediterranean climates, could well become a permanent feature of the English countryside.
Huggits Farm Olives
More articles on: olive oil, olive oil production, olive tree cultivation, United Kingdom
Olive Oil Producers in Argentina Hope to Follow Malbec's Lead
Olive Oil Producers in Argentina Hope to Follow Malbec's Lead
Improving quality is the best way to improve Argentina's international standing, said small and large-scale producers in a documentary video produced by Olive Oil Times that debuted today.
By Daniel Dawson on July 9, 2019
Filed in Business
Italian Producers Explain What It Takes to Succeed in a Tough Campaign
Harvesting at the right time, proper stewardship of the land, attention to details, teamwork, perseverance, and passion were all listed by award-winning Italian producers as the reasons for their success.
By Ylenia Granitto on June 25, 2019
Africa and The Middle East:
Egypt Wants to Plant 100 Million Olive Trees for Oil Production
While Egypt is the world's second largest producer of table olives, very little infrastructure in the country is dedicated to olive oil production.
By Julie Al-Zoubi on May 20, 2019
Filed in Africa and The Middle East
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Vote Bill Conley for Liberty Mutual Coach of the Year
Bill Conley (courtesy Joe Volpi)
BOSTON – Ohio Dominican football fans can vote for coach Bill Conley to be the 2013 Liberty Mutual Coach of the Year, the only college football award that supports both charity and scholarship through its winners by celebrating one coach at each college level who best exemplifies responsibility, integrity, sportsmanship and excellence both on and off the field.
Each of the Liberty Mutual Coach of the Year winners from four college football divisions – FBS, FCS, Division II, and Division III – receives $50,000 from Liberty Mutual Insurance to donate to the charity or charities of their choice.
Now through December 3, fans can visit CoachoftheYear.com to cast their vote for their favorite coach in each of the four divisions. Additionally, fans can also vote for the first time on Facebook at Facebook.com/LMCoachoftheYear, where they also are encouraged to rally support for their coach through their social community.
Winning coaches will be announced January 6, 2014, prior to the national championship game.


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Nickolas Robert Diaz (Stockton, 2 augustus 1983) is een Amerikaans MMA-vechter. Hij is een voormalig Strikeforce- en WEC weltergewicht kampioen. Diaz heeft een zwarte band behaald in Braziliaans jiujitsu onder leiding van Cesar Gracie. Hij is de oudere broer van MMA-vechter Nate Diaz.

Carrière
Diaz maakte in augustus 2001 op 18-jarige leeftijd zijn professionele MMA debuut in de Californische IFC-organisatie. Hier won hij zijn eerste wedstrijd en werd na zijn tweede wedstrijd al kampioen, na het verslaan van Chris Lytle. In 2003 werd Diaz WEC weltergewicht kampioen na het verslaan van Joe Hurley door middel van een submissie (armklem). Hetzelfde jaar kreeg hij een contract bij de organisatie UFC. In de volgende drie jaar, had Diaz tien wedstrijden bij de organisatie waarvan hij er zes won. Hij vocht onder meer tegen Robbie Lawler, Sean Sherk (voormalig lichtgewicht kampioen) en Diego Sanchez (winnaar van The Ultimate Fighter).

Diaz verlengde zijn contract bij de UFC niet en tekende vervolgens bij de Japanse organisatie Pride, waar hij het opnam tegen Takanori Gomi in februari 2007. Diaz won door middel van een submissie (wurggreep), maar de uitslag werd later omgezet in een no contest omdat Diaz positief testte op THC. Hierna vocht hij voor de EliteXC-organisatie waar hij in september dat jaar debuteerde.

In januari 2010 werd Diaz kampioen in de organisatie Strikeforce na het verslaan van Marius Žaromskis door knock-out. Diaz won zijn tiende opeenvolgende wedstrijd en verdedigde zijn Strikeforce-titel voor de derde keer toen hij Paul Daley versloeg in april 2011.

Diaz had een gevecht tegen Carlos Condit voor een interim-titel tijdens UFC 143 op 4 februari 2012. De wedstrijd doorliep alle vijf rondes en werd gewonnen door Condit na een unanieme jurybeslissing.

Diaz nam het op tegen toenmalig UFC kampioen weltergewicht Georges St-Pierre voor de titel op 16 maart 2013, en verloor de wedstrijd door een unanieme beslissing.

Op 31 januari 2015 ging Diaz het gevecht aan tegen voormalig UFC middengewicht kampioen Anderson Silva en verloor weer door middel van een unanieme beslissing, maar de uitslag werd kort daarna omgezet in een no contest omdat Silva positief testte op anabole steroïden. Diaz testte positief op THC, waarvoor hij door de NSAC voor vijf jaar werd geschorst en een boete kreeg opgelegd van $165.000.

Amerikaans MMA-vechter


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The Vought F4U Corsair
Martin W Bowman
First flown in 1940, the Vought F4U Corsair instantly became the fastest fighter in the world and the fastest US aircraft of any description. The F4U entered service with the US Navy in 1942 and came to be extensively used from land and sea.
120 color & black and white illustrations
Dimensions : 9.5 X 9.5 inches
First flown in 1940, the prototype Vought F4U Corsair instantly became the fastest fighter in the world and the fastest US aircraft of any description. Powered by a huge 18-cylinder Pratt and Whitney Double Wasp engine driving an enormous 13 feet 4 inch propeller, the first Corsairs were capable of 417mph. This figure would rise to nearly 450mph in later versions.
Production began in 1941, not only by Vought but also by Goodyear and Brewster, and the F4U entered service with the US Navy in September 1942. The aircraft subsequently came to be extensively used from land and sea by the US Marines, Royal Navy and Royal New Zealand Air Force. Famous squadrons like VMF-214 'The Black Sheep' and VF-17 'Jolly Rogers', along with many others, maintained total ascendancy over the Japanese for the rest of the war - a remarkable achievement for a single type.
After the Second World War the Corsair remained in production and was used with distinction by the French in Indo-China and again by the US Navy in Korea. Since then Corsairs have achieved significant success in air races and more and more are being restored to fly for museums and warbird enthusiasts the world over.
This comprehensive new book combines technical information and detailed development history with a fascinating combat history told, in many cases, by the Second World War and Korean War pilots themselves. Well researched, readable and illustrated with scores of rare and previously unpublished photographs, The Vought F4U Corsair is the perfect book for any fan of the 'bent wing bird'.
Martin Bowman is one of Britain's leading aviation authors and has written a great deal of books focussing on aspects of Second World War aviation history. He lives in Norwich in Norfolk. He is the author of many Pen and Sword Aviation titles, including all releases in the exhaustive Air War D-Day and Air War Market Garden series.
The Vought F4u Corsair Reviews
Watford & South West Herts in the Great War
Eugenia Russell, Quentin Russell
Last Assault
Charles Whiting
Salute to One of 'The Few'
Simon St John Beer
Sierra Leone: Revolutionary United Front
Al J. Venter
Victoria Crosses on the Western Front - April 1915 to June 1916
Paul Oldfield
Postcards of the Army Service Corps 1902 - 1918
Armoured Warfare and the Waffen-SS 1944-1945
Anthony Tucker-Jones
The Middlebrook Guide to the Somme Battlefields
Martin Middlebrook, Mary Middlebrook


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Home Pakistan Defence Forum > Country Watch > Indian Defence Forum >
When IAF Canberra was shot down by paf
Discussion in 'Indian Defence Forum' started by Meliodas, Apr 26, 2019.
Apr 26, 2019 #1
Meliodas FULL MEMBER
Untold story of an IAF Canberra & its crew, 60 years before Wing Commander Abhinandan's MiG
SNEHESH ALEX PHILIP
New Delhi: When Wing Commander Abhinandan Varthaman, flying a MiG-21, downed a cutting-edge F-16 of the Pakistan Air Force (PAF) during the 27 February dogfight, it marked a major feat in India's air-defence capabilities.
It also bore several similarities with an incident with Pakistan six decades ago, when that country first scored a hit against an IAF aircraft. The April 1959 operation ended with Pakistan shooting down an Indian Air Force (IAF) plane, detaining its two pilots and repatriating them to India, just as happened with Wing Commander Varthaman.
The operation in question took place on 10 April 1959, as India deployed an English Electric Canberra PR 57 aircraft to conduct a photo-reconnaissance over Jammu & Kashmir and Himachal Pradesh.
The morning of 10 April 1959
It was Eid, and most PAF personnel had been given the day off, with a skeleton crew, mostly comprising unmarried officers and airmen, on duty.
They included Flight Lieutenants Mohammad Yunis and Naseer Butt of 15 Squadron (the unit also known as the "Cobras"), stationed at Peshawar.
As Yunis and Butt sat drinking tea, the radar operator, Pilot Officer Rab Nawaz, operating a Second World War-era radar set, announced that an "intruder" had flown in from Indian airspace, towards Gujrat in Pakistan's Punjab province.
The pilots scrambled two F-86F Sabre 55-005, which had then recently been given to Pakistan by the US — the two countries were at the time part of the erstwhile Cold War-era security alliance CENTO.
"At about 20,000 ft, we spotted a double trail way above and far ahead… When we were at 41,000 ft, the trails could be identified as a single Canberra flying on a steady northerly heading, clearly oblivious of any threat to it," Yunis wrote of the incident.
"Overhead Gujrat now, it appeared to be at about 50,000 ft. We punched our tanks and, although we were still out of range, the mounting excitement threatened to get the better of sound judgement," he added.
Yunis called for clearance to shoot and, for a moment, Nawaz debated whether to go through the full standard procedure for obtaining permission, according to the account. But that would very likely have allowed the Canberra to slip away, Yunis wrote, and Nawaz then took a decision on his own and cleared the sabres to shoot.
"We were still not within optimum range but Naseer impatiently launched into a series of energy-climb/burst-of-gunfire/stall out sequence which became more desperate with each repetition," he wrote.
"In the meantime, I kept a steady height and heading in order to give rear cover to my leader. It suddenly occurred to me that, if the Canberra spotted us, he would in all probability turn right i.e., towards the border, so I eased over in that direction," he added.
"The leader had given me the okay to have a go if I could, but I could see I was still too far below the target."
"Presently, the Canberra did turn right and then, as if he had spotted me, quickly reversed. On that side he must have spotted [Naseer] Butt, for he seemed to panic and tightened his turn, which of course caused him to lose height rapidly," Yunis wrote.
The account then retells how the Indian aircraft was battered and its pilots forced to reject.
"I saw my chance and put a bead on his right engine — just in time I remembered my Hunter wingspan setting and quickly ranged on half the Canberra's span — immediately I could see my bullets impacting on his right engine," he added.
"I traversed the bead to the centre, not letting go of the trigger till the guns stopped — due to over-heating, as it turned out. But I had fired 1,200 rounds by then and the doomed Canberra whipped into a spiral," he wrote.
The pilot of the Canberra, Sqn Ldr J.C. Sengupta, and navigator Flt Lt S.N. Rampal were taken into custody after they ejected and landed on Pakistani soil. However, they were repatriated a day later.
Sqn Ldr Sengupta retired as a Group Captain in 1976 while Flt Lt Rampal retired as Wing Commander in 1971.
Also read: Wing Commander Abhinandan being shot down is the real Rafale scandal
What the Indian government said
Addressing the Lok Sabha a day after the incident, on 11 April 1959, India's then defence minister V.K. Krishna Menon said the Indian aircraft was engaged in taking aerial photographs over Himachal Pradesh and Jammu and Kashmir when it strayed across the border.
Explaining the presence of the aircraft in the Pakistani air space, he referred to the height that the Canberra was flying, saying that a navigational mistake could easily be committed at that altitude.
"The straying of our plane from our airspace was not and could not be part of any hostile design or policy," he added.
Saying he doubted whether Pakistan had issued any warning before they shot down the Indian aircraft, he described the action as unwarranted and contrary to international law and custom.
The Canberra aircraft
Canberra, the only bomber of the IAF until the late seventies, came to India in 1957. An aircraft of British origin, the high-altitude bomber was a force to reckon with in the war scenario prevailing in the Indian subcontinent at the time.
Capable of cruising at four-fifth the speed of sound at 40,000 feet, Canberra was the right weapon to carry the war well beyond the frontiers, and deep into enemy territory.
Negotiations to acquire the Canberra began in 1954 and, in January 1957, an order was placed for 54 aircraft, including eight PR57 photo-reconnaissance aircraft, and six T4 training aircraft.
The deliveries began in the summer of that year.
Squadron No. 5 of the IAF was the first to get the Canberra bomber, in May 1957. The eight PR-57s were operated by the 106 SR Squadron, while the T4 aircraft were formed into a jet-bomber operational conversion unit (JBCU), which is basically a training formation.
The Canberras were mostly based at Agra. The target-towing version was added to the IAF fleet in 1975.
Over the years, Canberra became the backbone of the IAF for bombing raids and photo-reconnaissance operations. On 11 May 2007, the IAF retired its Canberra aircraft after 50 years of service.
Canberra's mettle was tested for the first time during the 1965 Indo-Pak war, as numerous high-level bombing missions were successfully flown during the course of the short war.
In recognition of their bravery and valour, several Canberra crew members were awarded Maha Vir Chakras and Vir Chakras — the second- and third-highest wartime gallantry awards — among other decorations.
However, the battle scenario changed dramatically in the following years with the entry of radars and high-performance fighters with night-combat, reducing the potential of the Canberra.
However, the air space closer to the ground was still a grey area for the radars and the fighters, and this was exploited well by the Canberra.
When the 1971 war between India and Pakistan broke out, within hours of a Pakistani pre-emptive strike, the Canberra spearheaded a strong counter-attack on a number of Pakistani targets.
Throughout the war, the Canberra gave the enemy no respite, even at night, with its persistent raids on numerous targets.
The highlight of the 1971 operation were the bombing attacks carried out over the Oil Refinery Complex at Karachi, which proved so devastating that the target was ablaze for nearly a week.
Canberra squadrons were rewarded with four Maha Vir Chakras, a dozen Vir Chakras and many other gallantry awards and distinguished service awards in recognition of their contribution to the 1971 war.
Whirling_dervesh FULL MEMBER
When Wing Commander Abhinandan Varthaman, flying a MiG-21, downed a cutting-edge F-16
Why do Indians feel the need to keep massaging their bruised and battered egos. It's ok....hotaa hai....there's no shame. Repeating this over and over again wont change facts.
waz MODERATOR
New Delhi: When Wing Commander Abhinandan Varthaman, flying a MiG-21, dreamt he downed a cutting-edge F-16 of the Pakistan Air Force (PAF) during the 27 February dogfight, it marked a major feat in India's bull$hitting capability.
Thanks x 12
Mig hunter FULL MEMBER
When I read first line then I......... All know
I salute their resolve to follow Chankia theory in true letter and spirit... By lying, lying, and so on... They started lying from Pulwama to this date and they will keep on doing that I don't know till what point... And interesting thing is that their media print same story in different angle every time... Hahahaha looosers
war&peace ELITE MEMBER
BHINDUS day-dreaming & wetting their dhottis for downing an imaginary 40 years old PAF F-16 and that imaginary F-16 becomes a cutting edge... While in reality PAF downed their MiG-21 Bison recently upgraded to 4th gen standards and Su30 MKI upgraded recently to supersukhoi standards and they used to call it mini AWACs but since then they lamenting the absence of Rafales and S-400 etc etc.
Do we have any veteran who can confirm or deny the contents of this story by print. It would be interesting to see how politicians and media of both nations handled the situation.
Max SENIOR MEMBER
Meliodas said: ↑
Mallu bhaiya still busy finding solace?
the simple solution do not pay attention to these BS.... Indians despite knowing every thing ....will continue with fake reports ....almost every day. they will keep saying F16 HIT ...BALAKOT HIT ...you may listen these two claim in their upcoming movies...as masala song...that will be super hit ...this all drives from the reaction of General public...and currently the indian general public willing to buy this churan.
graphican ELITE MEMBER
India lost a Tejas, Arjun tank, and the little brother of Nirandra Modi to PAF on 27th Feb. Everthing was shot down by the same F16 Indians shot a minute later and everything fell in Indian occupied Kashmir. Sorry Pakistan don't have anything to show as F16 and its data is no more and India wouldn't show pictures of their lost assets out of shame.
"Anj way tay anj hi sahi". If fabrication is the name of the game then welcome to it!
Apr 27, 2019 #10
Dubious MODERATOR
waz said: ↑
Psychic SENIOR MEMBER
It did not cross the line a few miles;
It came over Rawalpindi deep inside Pak air space for photo-recce and was definitely an intruder. It wasn't the first time it crossed either.
Intruder aircraft will be shot down: IAF.
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