In the sleepy Gloucestershire village of Wotton-under-Edge, something beastly has been stirring for centuries and centuries. Welcome to the Ancient Ram Inn, a twelfth century bed and breakfast that is believed to be Britain's most haunted hotel. This quirky little Cotswold cottage is thought to be haunted by no fewer than twenty separate ghouls and spectres.
It might sound like a bit of harmless fun, but you'd be shocked at the things that go on here. The owners of the B&B have welcomed thousands of curious visitors over the years, but most seem to have bitten off more than they can chew. One visitor, who unsurprisingly wishes to stay anonymous, actually leaped from a first floor window in fright after he heard the sound of a child screaming.
There have been no injuries as a result of the ghostly activity at the Ancient Ram Inn, but the spooky figures that walk its staircases, hallways and guest bedrooms certainly aren't trying to make friends. Caroline Humphries, whose family has lived in the inn for almost fifty years, has heard every story in the book. She's regularly inundated with tales of crying spectres, hovering apparitions and screaming ghouls who don't seem to enjoy the constant company.
It's very easy to take the stories that surround the Ancient Ram Inn with a pinch of salt, but they are extremely regular and extremely pervasive. Lots of guests have reported the same sightings, figures and sensations during a stay at this otherwise rather charming hotel. It doesn't help that the B&B is confirmed as being built on the sight of a pagan burial ground. Or that Caroline Humphries' father stumbled upon a series of daggers and small bones buried at the base of the building.
The Ancient Ram Inn is so haunted that its spectres have become rather familiar to the many ghost investigators and horror writers who regularly stay there. One of these spectres is referred to as Rosie – she is believed to be the spirit of a young girl murdered on the original burial site. There's even rumour of an incubus wandering the bedrooms of the inn, looking for vulnerable prey.
If you don't know what an incubus is, don't worry – all that you've got to know is that you do not want one in your bedroom. An incubus is the male equivalent of the more well known succubus – a disgruntled spirit that shows a disturbing proclivity for inter-corporeal relations. Let's be honest, you haven't seen anything until you've been confronted by a horny ghost. And yes, it has happened to a guest at the Ancient Ram Inn.
An unfortunate female guest was reportedly pushed onto a bed by an unexplained presence, before presumably fleeing and never returning to Wotton-under-Edge again. Guests have reportedly seen furniture fly around bedrooms, small children wander the hallways and wooden beams vibrate as if electrified. Despite this, eager ghost hunters are queuing up for a room at the Ancient Ram Inn.
It's just as well really – the Humphries have become rather resigned to the idea that nobody will ever buy their little hotel. After fifty years of things going bump in the night, the Gloucestershire family have gotten used to their ghostly visitors. They still don't like them, but they've carved out a strange symbiotic existence with these belligerent ghouls. Carol Humphries now runs the B&B as a specialist hotel, designed for those seeking paranormal experiences.
Do you think that you could last the night at the Ancient Ram Inn? You might believe that you have nerves of steel, but there's a good chance that you'll be out of the door just as soon as that first cold hand wraps around your ankle. These are the things that nightmare are made of – dare you spend an evening with them?
Next post Paranormal Activity 5 - Prepare To Be...Bored?


We have had some interesting news today in the world of Xbox, rumours quashed and possible rumours sparked. Larry Hyrb, better known as Major Nelson and Phil Spencer the Corporate Vice President of Microsoft Games Studios, took to the internet to answer a few questions.
Major Nelson wasn't left out either, he took to Reddit to answer a very interesting question, "Now that any Xbox One can be used as a dev kit it is possible for mod support now?" He replied with "It's up to developers to support that. These are two very different things." This leads me to believe that if Developers allow us to mod their games we may be subject to certain limitations, such as using their own tools and utilities.
So it appears Microsoft are really working hard to win over the Indie Developers and the future about DRM being applied is still in the balance, and let us not forget about the mods, will we see them supported when the Xbox One comes around? Only time will tell.


We created a Turmeric Shot infused with Ginger so that you can enjoy the benefits of both roots, formulated into one convenient little bottle. Turmeric contains a powerful compound called Curcumin, boasting a multitude of researched incredible medicinal properties. Due to its high anti-inflammatory effects, it's been proven helpful for conditions such as arthritis and rheumatoid arthritis, acting as an all-natural alternative to supporting good health.
Ginger boasts incredible medical properties including anti-inflammation and antioxidant effects, helping to fight infection such as the flu and common colds, aid indigestion and even relieve nausea. Ginger is also terrific in supporting a natural means of weight loss. Regular consumption can reduce muscle pain and discomfort, as well as improve the health and wellbeing of the brain.


Banning vaporizers will cost lives and India if the government follows through with proposals to prevent e-cig devices from hitting the market.
Scientists are pleading with local authorities to conduct "India-centric research before deciding on any hasty move".
The smoking rates in India are dangerously high, showing no sign of stagnation.
According to the World Health Organization, India is home to 12% of the world's smoking population and face over one million annual tobacco-related deaths. Moreover, a staggering 30% of India's male population smokes cigarettes.
Last year two scientists wrote a heartfelt letter to the Union Health and Family Welfare Minister J.P. Nadda urging him to reconsider policies that facilitate smoking cessation.
R.N. Sharan of the Department of Biochemistry, North-Eastern Hill University (NEHU), and M. Siddiqi, Chairman of Cancer Foundation of India aim to provide smokers with safe and regulated alternatives to tobacco.
"The Electronic Nicotine Delivery System (ENDS), more popularly known as e-cigarettes, offer a safer and effective way of meeting the physiological demands of nicotine to smokers to help quit or cut down smoking significantly," they said, after pointing out that in countries like the UK, vape products are freely available and have led lead to a significant decline in smoking.
A study conducted in 2016 found that increasing vaporizer accessibility in India would decrease the number of smokers by 50%. This equates to saving around 90 million life-years.
Moreover, US non-profit organization Reason Foundation carried out a study that found the results of vape products becoming more accessible and competitive sale prices in India would lead to more smokers switching to vaping.
The researchers also calculated that the number of smokers could decrease by 50% or more within the next 20 years. "In 30 years, vaping might eliminate smoking altogether," concluded the study.
Alas, all of the enlightening data that should be contributing to reversing the problem is getting the cold shoulder.
India has a disastrously serious tobacco problem and, considering India is the second largest cigarette smoking population in the world, it's time some action is taken.
Earlier in 2017, the Union Health Ministry amassed a working group that would assess the effects of vape products on locals in an attempt to discover whether a vape ban is the best course of action.
This committee concluded that the products have cancer-causing properties and are highly addictive. This was the basis on which the Union government seeks to implement a ban.
How does allowing cigarette products to be sold make any sense using this logic?
Dr. Siddiqi and Prof. Sharan are again using their voices and professional opinions with hopes to knock some sense into the government. The pair of scientists have written yet another letter addressed to the Union Minister, pleading again with him to avert a public health disaster by regulating vaporizers rather than ban them.
"We believe that public health in India is at a greater risk under a prohibitive environment than by allowing smokers, who wish to cease tobacco use, an alternative option based on nicotine replacement via e-cigarettes." said Prof. Sharan.
The scientists pointed out that vape devices have shown evidence leading to decreased smoking rates in countries that have endorsed vaporizers. In fact, the UK, who has fully endorsed vaping devices as part of smoking cessation programs, is reporting the lowest number of smokers ever recorded.
Should India be more open to regulation? Would a vape ban risk lives in India? Did the smoking rates in India shock you?


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Online Bachelor of Arts in World Religions
Please select... Bachelor of Arts in World Religions
Understand the Role of Religion in the Development of Profound Human Culture.
Earn your Bachelor of Arts in World Religions from top-ranked LSU Alexandria Online. Our program provides students with an opportunity to study the major belief systems and scriptures that have operated in human history and to prepare for successful careers in a wide range of fields, including journalism, politics, education, foreign affairs, nonprofit businesses, and publishing. Program graduates may also advance to more focused study in graduate school, professional school, or divinity school.
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Students of the bachelor of arts program in world religions can expect to gain a comprehensive, comparative, and thorough understanding of the major religious belief systems and sacred scriptures, including their origins and evolution, that have impacted human history.
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Developed from experienced faculty, our curriculum for the B.A. program in world religions will additionally study the rituals, practices, and sacred texts that have shaped culture and profound human thought.
The final cost of this program will be determined once all transfer credits are evaluated. For the BA in World Religions program, you are able to transfer up to 75% of previous credits.
RELG 1004 - Old Testament | Credit Hours: 3
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RELG 3029 - Judaism, Christianity, and Islam | Credit Hours: 3
RELG 3030 - Religion in America | Credit Hours: 3
RELG 4001 - History of Christian Thought | Credit Hours: 3
RELG 4010 - History of Christian Thought to the 16th Century | Credit Hours: 3
RELG 4227 - Contemporary Christian Thought | Credit Hours: 3
RELG 4027 - Asian Religions | Credit Hours: 3
World Religions Electives | Credit Hours: 9
Foreign Language Electives Credits: 12
12 hours in one language or 9 hours in one language and 3 in another or 6 hours in one language and 6 in another.

redpajama_set_name: RedPajamaCommonCrawl


Cardi B's Government Shutdown Rant Turned Into a Song!
Cardi B's rant about the government shutdown has gone viral and someone just turned it into a song!
The Gregory Brothers, who are famous for "making songs out of things that were previously not songs," posted this awesome video on their account.
Next Post: Ryan Reynolds Heads Out for the Day in Chilly New York City!


By an odd coincidence, I watched Braveheart on the same day as Michael Collins, which offered, by way of contrast, a useful insight into why Neil Jordan's influential biopic of one of the heroes of Ireland's fight for independence just doesn't work.
Michael Collins begins with the Easter Rising of 1916 and the subsequent shootings in Kilmainham Gaol, which caused public favour to fall behind the revolutionary nationalists. The film then jumps forward to 1918 and the beginnings of the War of Independence and then into the Treaty, which caused the partition of Ireland as well as the end of the war. Shortly after came the Irish Civil War between the pro-Treaty government of the Irish Republic, led provisionally by military chief Michael Collins, and the anti-Treaty rebels, incensed by the partition and Ireland's status as a dominium of Britain, led by Éamon de Valera, Collins' old commander and previously the President of the Irish Republic. Collins was killed at the age of 31 in Béal na mBláth, near where he was born, during a tour of West Cork.
The above is more of a potted history of Michael Collins than a description of the plot of the film, though it offers about as much insight into the history of the time as the film does. The film is primarily about the events and the circumstances and it will introduce novices to such things as the Black and Tans, the Twelve Apostles, the Cairo Gang, and the Bloody Sunday of 1920. But it does not go much further than that, readdressing each event from the perspective of an international audience and the rules of the three-act structure. As a result, it rushes through the events and loses sight of, as the title would suggest, its main subject. Michael Collins (Liam Neeson) is a bit of a cipher in the film, as is Éamon de Valera (Alan Rickman), played, for the sake of simplicity, as egotistical and unrepentantly evil. In Neil Jordan's carefully timed and paced version of events, both characters move between a variety of registers without much sense of an inner consistency. Every decision that de Valera makes seems ridiculously stupid but it couldn't be otherwise because the film spends no time whatsoever in trying to create a sense of what he is thinking of. Similarly, Collins is played as a fun-loving joker who will do anything that a man has to do for freedom but will be alternately ruthless and depressed about it. In Jordan's rigid subservience to historical events, simplicity and pacing, he does both of them, and nearly every other character, a disservice.
Compared, oddly, to Braveheart, which has been named one of the most historically inaccurate films of recent times, but that nonetheless managed to convey the passions and sacrifices of a fight for national independence. Although the real events and the real life Wallace's character are far from what the film represents, the film succeeds nonetheless as a rousing entertainment of national heroism overcoming foreign subjugation. If the facts are false, at least the emotions are not and the film represents such nationalism, as it must feel to those committed to national struggles. None of this matters as much as the fact that we never lose sight of William Wallace, the character as played by Mel Gibson. We see his inner struggles, his dark almost animalistic side, his wisdom and his refusal to deny his beliefs – and, crucially, we see how the myths that generate about Wallace are comically inaccurate. The film is simplistic, overly macho and full of falsehoods, but it is a fantastic, old-fashioned epic.
Michael Collins, meanwhile, is not. It works neither as a historical record of the events nor as a hagiographic, rousing spectacle. It ends up being a toothless beast, which fails to bring across either the character of the man (neither fictional nor real) or the significance of the events depicted. It refers to the events with an emphasis that excludes all else, but presents them so simplistically in shades of black and white that nothing of interest can be learned from them. It also refigures them to assist the sentimentalised drama and the running time. Neither does it get a sense of its characters, being both hagiographic in its attempts to make a hero out of Michael Collins and also assisting in mystifying the character more through its terribly inconsistent and unfocused characterization. Short, sporadic scenes will show a likable Collins at home and enjoying himself, but these scenes don't add up and often feel misplaced. Elsewhere, the film shows only a man going through historical motions.
Only rarely does Jordan's strategy make sense. We briefly get a sense of one of Jordan's apparent trademarks, the moral complexity in his work. There are scenes that seem intended to present Collins ambiguously – leaving it up to each member of the audience to decide whether certain actions are justified or not. But these scenes are given short shrift and are only noticeable if you look for them and the rest of the film mainly contradicts them by either making it clear that Collins has no choice or guiding the argument with a simplistic representation of what is at stake. It both makes a saint of Collins and loses sight of him. The film is hence neither rousing or moving nor informative or interesting. It succeeds in being only bland.
Forgive Braveheart its falsehoods and it is a great film made in the old style – not to be taken seriously and a little hokey, but something to be swept up in and enjoyed – the kind of underdog story that Hollywood has forgotten to make since America now makes war with the underdogs. Michael Collins, meanwhile, forgiving its falsehoods, is far from emotionally engaging. Neither is it interesting in its complexity or in its storytelling. It is a grey, cold, dull film that is both contrived for the sake of entertainment and wholly lacking in entertainment.


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Az utolsó titkos ügynök (eredeti cím: The Last Mercenary / Le dernier mercenaire) 2021-ben bemutatott francia akció filmvígjáték, melynek rendezője David Charhon, forgatókönyvírója Charhon és Ismael Sy Savane. A főszerepben Jean-Claude Van Damme, Alban Ivanov, Assa Sylla és Samir Decazza látható.

A filmet 2021. július 30-án mutatta be a Netflix.

Cselekmény

Egy legendás, egykori titkosszolgálati ügynöknek, akit "Köd" néven ismernek (Van Damme) sürgősen vissza kell térnie Franciaországba, amikor számára ismeretlen fiát a kormány egy elszánt bürokrata intézkedése és egy maffiaakció következtében tévesen fegyver- és kábítószer-kereskedelemmel vádolja meg, pedig csak névazonosság áll fenn egy valódi bűnözővel.

Szereplők

Fordítás

Jegyzetek

További információk

2021 filmjei
Francia akciófilmek
Francia filmvígjátékok
A Netflix saját gyártású filmjei

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Now I know there is one in Melbourne, but we've had some pretty bad weather of late and when it's been good I haven't had the chance to go. In Sydney the store was completely empty, and I couldn't decide what to get and, unlike usual gelatarias you're not able to try any of the flavours.
After uming and ahing for a few minutes, the girl behind the counter told me if I installed and logged onto the Airbnb app I'd get a free scoop of the Go Bush, a caramel and wattle seed gelato with crushed macadamias.
It was nice, burnt and bitter coffee flavours with macadamias crushed here and there, but to be honest it wasn't anything special.
Gelato Messina is the new golden child in Artisan gelato with The Good Food Guide voting them the "Best Gelato in Australia".
Having heard tales of the lines and their greatness I just had to check it out while I was up in Sydney. I walked in and it was heaven, they offer 20 odd flavours with sorbets as well.
After once again trying several (coconut and pandan, salted caramel, raspberry, hazelnut, salted coconut and mango to name a few) I settled on the chocolate fondant, pistachio and Fo' Shizzle, a caramel custard ice cream with raspberry sauce, chocolate chips and hazelnuts.
The gelato was fantastic; the chocolate fondant was rich and dark, the texture was a cross between a brownie and mousse.
The Fo' Shizzle was equally good but slightly harder to describe, it was a complete mash up of flavours, everything worked in it and tasted great.
After eating these two, the pistachio was kind of boring. That's not to say it wasn't a great gelato, I mean it was heavily flavoured, creamy and equally rich, but in the end not as memorable as the other two.
After grabbing a business card I realised one had recently opened up in Melbourne and went to check that out once I was back.
I went back for more chocolate (it's hard to say no to) and went for the Cremino, a caramel based gelato with ginger nut, meringue, chocolate and peanut butter sauces and chopped up peanuts. It was lusciously delicious, the ginger nut and meringue especially gave it a distinguishing flavour and texture. Unfortunately the Cremino and Fo' Shizzle are part of the ever changing specials and come and go somewhat like the Melbourne sun.
I stumbled upon this café on my first day in Sydney and told myself I would be back, and on my last day I braved the rain to find it again.
It was a well refined café, wood and brick with a heavy country vibe, and judging by both the menu and the shelves there was a big focus on quality and organic ingredients.
I took a seat and ordered a long black with my breakfast, the balsamic and rosemary mushrooms with fetta and gremola.
The Long black was great, possibly the best of the trip. It wasn't overly hot with no harsh bitterness either.
As I sipped my drink I noticed the place was packed, mostly with business type personnel. I sat there pondering how that was at 11:15 in the morning, and wondered if people in Sydney just don't work.
Just as I finished my coffee the meal arrived, it looked and smelled great with strong vinegary tones.
Taking my first bite it was oily, sour and rich. The cheese had slightly warmed from the mushrooms, and the gremola was fantastic giving nice texture and taste along with a very distinct rosemary flavour. To top it all off the mushrooms were roasted fantastically with there being plenty of them in one serving.
I really liked Pablo and rusty and would definitely go back if I was to ever (hopefully never) live in Sydney.
I was on my way to the chophouse when I spotted this place, from the outside all I could see was a very classy looking bar, slightly retro with a 1920's feel and dimly lit. I walked in and got past the bar and it became a run of the mill restaurant with a very business vibe.
I took a seat against the back wall and ordered the chicken liver parfait served with Dijon and toast points.
The place was pretty empty when I first arrived which made meant the food didn't take long, but when I left however the place was quite packed.
When the meal arrived it looked great, and the portion was also quite large.
The taste was fantastic, and when paired with the drizzle of balsamic and Dijon mustard created a rather divine flavour and texture but still was not as creamy as the Madame Sousou one I had (HL).
York is a really cool place with a fairly comprehensive and slightly adventurous menu and hopefully next time I'm in Sydney I'll get to the steak.
Posted on December 13, 2013 October 13, 2015 by AdrielPosted in Brunch n Lunch, SteakLESS, SydneyTagged Gelato, Sydney.


This page is in the process of being revised for 2018–2019. Please stay tuned!
Composers Today is a State project that stimulates teachers and pupils toward greater creative efforts. Compositions are rated by professional composers. All entries are awarded a Certificate of Participation. Students may have the opportunity to perform for the Branch, and State winners are invited to perform at the MTAC State Convention. Contact the chair for rules.


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HomeTechnical TermsMacintosh Definition
Macintosh is a line of desktop and laptop computers developed by Apple. Each Macintosh computer, or Mac, runs a version of the Mac OS, Apple's desktop operating system. Since 2001, all Macs have run Mac OS X, a redesigned version of the original Mac OS that was built from the NeXTSTEP operating system.
The original Macintosh, released in 1984, was the first personal computer to have a graphical user interface, or GUI. It was in all-in-one machine with a color display and included a mouse and a keyboard. Over the past several decades, Apple has released many new types of Macintosh computers, including all-in-one models, system units (which do not include a monitor), and portable computers.
The current Macintosh line (as of early 2012) includes the following models:
Mac Pro - a professional desktop computer sold as a system unit
iMac - an all-in-one desktop computer aimed at home and pro users
Mac mini - a small desktop computer designed for home and server purposes
MacBook Pro - a portable computer aimed towards students and pro users
MacBook Air - a lightweight portable computer designed for travelers
NOTE: While Macs are technically personal computers (PCs), the term PC is often used to describe computers that run Windows or Linux. Therefore, Macs are often referred to as personal computers, but not PCs. Unlike PCs, which are manufactured by several different companies, Apple designs and manufactures all Macintosh computers.
https://techterms.com/definition/macintosh
The definition of Macintosh on this page is an original TechTerms.com definition. If you would like to reference this page or cite this definition, you can use the green citation links above.
The goal of TechTerms.com is to explain computer terminology in a way that is easy to understand. We strive for simplicity and accuracy with every definition we publish. If you have feedback about the Macintosh definition or would like to suggest a new technical term, please contact us.
‹ Machine Learning | Macro ›


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Q: Using a For Loop in R to Make multiple use of different table variables using data$"variable" I'm trying to make use of the following
hd <- read.csv("mydata.csv") #read data into hd

x <- c("test_column1","test_column2") #Our vector with string names

test <- test = (head(x,2)) #this returns "test_column2"

barplot(100*prop.table(table(hd$test)))

I'm getting error "(converted from warning) no non-missing arguments to min; returning Inf"
I assume that is because it thinks that the variable is an integer.
Can someone explain to me?
This would work if it were (table(hd$test_column1)).
But I want to put in a variable after the $ so I can make a for loop to make multiple barplots

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Roslyn Moore - 'Hazy (A Desert Opera)'
Roslyn Moore is an up and coming artist with originality and who is earn a spot in your music library. Drawing influences from dark alt-pop, desert rock, sad-core, and trip hop, Roslyn's individuality shines through her music. Her latest album, 'Hazy,' does well to reach out to a variety of audiences while still showing what Roslyn's music and style are all about. 'Hazy (A Desert Opera) has all the elements actually found in modern-day opera - it has surprising melodies, well-composed songs, suspense and drama, heart-ache and rebellion, life and death, performed by extremely emotive vocals and atmospheric sound-production.
The album starts off with the song "Malibu" which commences with a heart-pounding motif of crawling notes that come into a three chord progression with hints of desert-rock like influences. One of the distinguishing factors in Roslyn's vocal style. In her song, "Malibu," Roslyn's vocals have a slight distortion adding an edgy quality to her music which is very effective. In addition, Roslyn adds layers of harmonies and vocal echoes which create an atmospheric mood that draws you in like a dream. The song melodies are alternative moving in and out of unexpected places. Laced with catchy lines like "Got my heart broken, but that's okay. Cause i'm still hoping for a better day And I'm in the blue skies of Malibu," Roslyn offers plenty to hold onto. "Malibu" is a great introduction to Roslyn's album that will have listeners hooked like a novel you can't put down.
Next on the album is the song "The Burbs." Kicking off the beginning of the song is a fantastic hook. Singing, "Out in the burbs, we do it right, yeah. We party right, yeah. The girls get it," Roslyn lays down a great groove. This well-produced song is extremely infectious and can hold its own against other top 40 artists. The fun lyrics paired with a memorable melody make for a great chorus that fans of all genres will be able to sing and move along to. "The Burbs" is sure to be a crowd favorite from this album and shows Roslyn's undeniable ability to appeal to a mass audience.
The title track of the album is "Hazy" is a picture-painting song about reaching the next level of "high." Hazy is a trip with a real psychedelic feel and lyrics that talk about hazy, spinning lights and a fear about where you are going as you reach for the top. The next song I'd like to mention from 'Hazy' is "Pretty Girls & Gin." This song begins with a lovely, melodic guitar intro before Roslyn comes in, starting the first verse, "Baby, can you take me outside? I don't really care where we go 'cause I've been flirting with heaven, but tonight I want to wander around in hell." The verses feature only guitar and voice, which paired with the simple melody of the vocals does a great job to highlight the pure and clear timbre of Roslyn's voice. "Pretty Girls & Gin" is a catchy and sad song that leaves a deep impact. It's about two people living like outlaws and surviving together. Roslyn has a brilliant way of capturing a moment with metaphor, "Can you take me to the Ferris wheel?"
The album seems to be leading to a fatal ending for the love hurt heroine as we arrive to the song "Suicide Note,"an emotional piece that talks honestly about depression and last words. Roslyn offers raw insight here on "trying to stay skinny and so fucking pretty" and "trying to stay perfect" for someone else. Here is a video of this brutally beautiful song:
Roslyn closes the album with a slower paced, emotional ballad, "Shampoo." The duet between guitar and voice acts as a great ending for the album, fitting after the track "The End," which featured more production. "Shampoo" pieces together a passionate, emotional story and delivers flawlessly with a great vocal performance. Roslyn takes into the most intimate place of love where the animal of love takes over and that place is our lover's scent which Roslyn sings about in "Shampoo."
Roslyn's album 'Hazy' is very clear; it is an album that showcases the many strengths and talents of a true artist who has written original songs that creep into the most intimate places of the raw heart with great-production and lyrics that are not afraid of expressing honest content delivered by vocals that shimmer with emotion on every single note. Roslyn has just finished a mini tour and is beginning work on her next music project. Her music is currently available on Sound-cloud.
#RoslynMoore #HazyADesertOpera
Summer Colds - 'Killing Flies'
Sharon Lia Band - 'Why Can't We Pretend'
Rasky Sadagr - 'Running With Scissors'


text:
\section{Introduction}
\label{s:Introduction}

The principle of \textit{polyrepresentation} \cite{Ingwersen:1996,Ingwersen:2005} posits that information retrieval (IR) effectiveness may improve through the consideration of multiple and diverse representations of information objects or processes, such as IR systems, models, or context about the documents and queries and their mediation to the user. This consideration is usually implemented experimentally as combination of diverse representations with respect to some criterion, more commonly retrieval effectiveness.
Whereas in principle, polyrepresentation aims to use different information object representations that may enhance IR performance, in practice, each representation is often associated with different degrees of uncertainty regarding the enhancement that it may bring. Therefore, if one representation is weaker (less reliable or accurate) than the others, this should be reflected in the combination process, otherwise effectiveness will suffer. In addition, given the potentially high-dimensional and noisy data contained in information object representations, many different combinations could be produced, fetching various different retrieval results. The process of deciding which combination of all is optimal for retrieval is an open problem, which was recently formalised by Lioma et al. (2010) \cite{Lioma:2010} in the context of polyrepresentation using a type of probabilistic logic called subjective logic \cite{Josang:2001}. This formalism was not accompanied by experimental evidence or practical applications.

This work can be seen as an extension of the work of Lioma et al. (2010); we provide a practical application of that model and show that it is experimentally validated. We show how to map the abstract notions of belief and uncertainty to real-life evidence drawn from a retrieval test collection, and how to estimate two different types of combinations for polyrepresentation assuming either (a) independence or (b) dependence between the information objects that are combined. We focus on the polyrepresentation of different types of context relating to user information needs (i.e. work task, user background knowledge, ideal answer) and show that the subjective logic model can predict their optimal combination prior and independently to the retrieval process. This finding holds for six different standard evaluation measures and two state of the art retrieval models. Furthermore, we look at non-commutativity in polyrepresentation in order to trace the bias between dependent contextual representations, and experimentally show its impact to retrieval effectiveness.

The remainder of this work is organised as follows.
Section \ref{s:RelatedWork} overviews related work.
Section \ref{s:Model} summarises the model of Lioma et al. (2010).
Section \ref{s:Experiments} presents the experimental evaluation of the model.
Section \ref{s:Discussion} discusses our findings and their limitations.
Section \ref{s:Conclusions} concludes this work.

\section{Related Work}
\label{s:RelatedWork}

There exist several applications of the principle of polyrepresentation to IR (see \cite{Frommholz:2010,Lioma:2010} for overviews). Regarding the application of polyrepresentation to user information needs, that is the focus of this work, most studies use polyrepresentation for query expansion. Diriye et al. (2009) \cite{DiriyeB:2009} apply polyrepresentation for interactive query expansion, i.e. to improve the suggestion of expansion terms to the users during their search in order to enable better retrieval performance. They show how providing supplementary information on expansion terms can address ambiguity and uncertainty issues, and can improve the perceived usefulness of the terms. In a different (non-interactive approach), Efron and Winget (2010) \cite{EfronW10} use polyrepresentation to combine so-called query aspects, which they collect by skimming the top $k$ documents retrieved for a given query. They consider these skimmed documents relevant and build pseudorelevance judgments without human intervention.

Our study differs from the above: we do not apply polyrepresentation for query expansion, but rather as a means to combine contextual evidence about the queries. This is more reminiscent of evidence combination or opinion fusion approaches, as explained below. The effect of capturing multiple representations of query context from a single user or from multiple users is a topic that has long attracted interest. According to Croft (2000) \cite{Croft00}, McGill et al. (1979) carried out a study of factors affecting retrieval by different users when they were assigned the same information need as a starting point. They found that there was surprisingly little overlap between the documents retrieved by the different users. Saracevic and Kantor (1988) \cite{ASI:ASI4} also found that when different users constructed Boolean queries based on the same descriptions of the information need, there was little overlap in the retrieved sets. One of the earliest perhaps retrieval models that explicitly incorporated the notion of multiple query representations was proposed by Turtle and Croft (1991) \cite{TurtleC91}. Soon after this, Belkin et al. (1993) \cite{BelkinCCC93} carried out a systematic study on the effects of query combination and verified that retrieval effectiveness could be substantially improved by query combination, but that the effectiveness of the combination depends on the effectiveness of the individual queries. Queries that provided less evidence about relevance should optimally have lower weights in the combination, because bad query representations could reduce effectiveness when combined with better representations.

This is exactly the topic we address in this work, based on the model proposed in Lioma et al. (2010). How to identify and separate `good' from `bad' query representations, so that undesirable combinations can be avoided, considering not only the features of the query representations, but also the way in which they are combined.

\section{Polyrepresentation Using \\ Subjective Logic}
\label{s:Model}
This section outlines basic notions in subjective logic and how it has been applied to polyrepresentation in Lioma et al. (2010). A detailed introduction into subjective logic can be found in J\o sang (2001) \cite{Josang:2001}.

\subsection{Subjective Logic Preliminaries}
\label{ss:SL-preliminaries}
The starting point is a frame of discernment defined over a proposition. Arguments in subjective logic are \textit{opinions} representing the belief that the proposition is true\footnote{The opinion space is a subset of the belief space used in the Dempster-Shafer belief theory \cite{Smets:1994}.}. An opinion is formally defined as $\omega^A_X$, where $A$ is the opinion owner, and $x$ is the proposition to which the opinion $\omega$ applies. An opinion can be decomposed into $\omega^A_x = (b,d,u,\alpha)$, where $b$ is $A$'s belief that the proposition is true, $d$ is $A$'s disbelief that the proposition is true\footnote{This corresponds to \textit{doubt} in Shafer (1976) \cite{Shafer:1976}.}, $u$ is $A$'s uncertainty about the proposition, and $\alpha$ is an a priori probability in the absence of committed belief mass.
Given a proposition for which we have an opinion, we can estimate the probability expectation that the proposition is true as:
\begin{equation}
\label{eq:expectation}
E=b+\alpha \cdot u
\end{equation}

If there is one opinion only about the proposition, Equation \ref{eq:expectation} is computed directly from the components of that opinion. If however several opinions exist about a proposition, assessing the truth of the proposition consists in fusing these opinions and estimating Equation \ref{eq:expectation} from their combined components. Subjective logic describes several fusion operators, suitable for different situations. Two of these are outlined next: \textit{consensus}, which combines independent opinions without bias, and \textit{recommendation}, which combines dependent opinions by modelling the influence of one opinion upon the other.

\paragraph{Consensus} Let us assume two independent opinions about a proposition $x$: $\omega^A_x = (b^A_x,d^A_x,u^A_x,\alpha^A_x)$ and $\omega^B_x = (b^B_x,d^B_x,u^B_x,\alpha^B_x)$. Their consensus  is $\omega^{A \oplus B}_x$ with components:

\begin{equation}
 \label{eq:consensus1}
b^{{A \oplus B}}_x = \frac{b^A_x u^B_x + b^B_x u^A_x}{\kappa}, \qquad d^{{A \oplus B}}_x = \frac{d^A_x u^B_x + d^B_x u^A_x}{\kappa}
\end{equation}

\begin{equation}
 \label{eq:consensus2}
u^{{A \oplus B}}_x = \frac{u^A_x u^B_x}{\kappa}, \qquad \kappa = u^A_x + u^B_x - u^A_x u^B_x
\end{equation}
\noindent
This operation\footnote{The consensus operator is similar to Dempster's rule \cite{Dempster:1968} (see \cite{Josang:2001}, Section 5.3, for a discussion on the difference between the two).} is commutative, associative and assumes that not all of the combined opinions have zero uncertainty. %

\paragraph{Recommendation} Let us assume two opinions that are not independent of each other, but where one influences the other. Let $\omega^B_x = (b^B_x, d^B_x, u^B_x, \alpha^B_x)$ be $B$'s opinion about the proposition, and $\omega^A_B = (b^A_B, d^A_B, u^A_B, \alpha^A_B)$ be $A$'s opinion about $B$'s recommendation ($B$'s influence to $A$). Their combination by recommendation $\omega^{A \otimes B}_x$ is $A$'s opinion about the proposition as a result of the recommendation from $B$, with components:
\begin{equation}
 \label{eq:recommendation1}
b^{A\otimes B}_x = b^A_B b^B_x, \qquad d^{A\otimes B}_x = b^A_B d^B_x
\end{equation}

\begin{equation}
 \label{eq:recommendation2}
u^{A\otimes B}_x = d^A_B + u^A_B + b^A_B u^B_x
\end{equation}
\noindent This operation\footnote{The recommendation operator can become equivalent to Shafer's discounting function \cite{Shafer:1976} as explained in Lioma et al. (2009) \cite{LiomaB:2009}, section  3.2.} is associative but not commutative.

\subsection{Subjective Logic for Polyrepresentation}
\label{ss:SL-preliminaries}
The model of Lioma et al. (2010) uses subjective logic to express the polyrepresentation of user information needs and their context. An analogy is made between opinions (from subjective logic) and query context representations (from polyrepresentation). The opinions express beliefs about the truth of a proposition. In this analogy, the proposition is the original query, and the opinions are the query context representations (see Table \ref{tab:analogies}). The opinion of a query context representation can be seen as the extent to which the query context represents the original query. Then, the combination of multiple query context representations can be modelled as fusing opinions about a proposition.

\begin{table}
\centering
\caption{\label{tab:analogies}Polyrepresentation and subjective logic analogies.}
\scalebox{0.75}{
\begin{tabular}{l|l}
\hline
POLYREPRESENTATION  &SUBJECTIVE LOGIC\\
\hline
$\cdot$ original query &$\cdot$ proposition\\
\hline
$\cdot$ query context   & $\cdot$ opinion\\
\hline
$\cdot$ how well a query context   & $\cdot$ belief, disbelief \& uncertainty of an  \\
represents the original query&opinion about the truth of the proposition\\
\hline
$\cdot$ combination of query     &\multirow{2}{*}{$\cdot$ opinion fusion}\\
context representations &\\
\hline
\end{tabular}
}
\end{table}

\section{Practical Application}
\label{s:Experiments}
In this paper, we implement and experimentally test the model of Lioma et al. (2010) summarised in the previous section. Given an IR test collection with rich query context representations, we convert these representations into subjective logic opinions and we produce their combinations using the consensus and recommendation operations. The strength of each combination is computed as a probability using Equation \ref{eq:expectation}, so that the higher the probability, the closer a combination represents the original query. To evaluate this model, we conduct retrieval experiments using the same query context combinations (completely independently from the subjective logic computations). If the best performing combination of query context (according to retrieval performance) corresponds to the combination of subjective logic opinions with the highest probability, we consider the model validated. In this work, we focus only on pairwise combinations of query context representations.

Section \ref{ss:Settings} describes the dataset and settings used in the polyrepresentation and retrieval experiments. Section \ref{ss:Experiments} presents the experimental findings.

\subsection{Experimental Setup}
\label{ss:Settings}

\subsubsection{Dataset and query context representations}
\label{sss:Dataset}
We use the iSearch test collection \cite{LykkeLLI10}, which consists of 46GB of scientific documents from the physics domain. iSearch comes with a set of 65 queries and their relevance assessments, which have been created by 23 lecturers and experienced postgraduate and graduate students from three different university departments of physics. The queries represent real information seeking tasks. The relevance assessment of each query was made by the same user who formulated that query, by examining a pool of documents retrieved for that query.

Each iSearch query contains the following five representations of different aspects of the user's information need and context:
\begin{enumerate}
\item user's verbose description of the information sought
\item background of the user's task
\item description of the user's current work task
\item description of the user's ideal answer
\item query terms (keywords) that the user might use in a search engine
\end{enumerate}

In this work, we consider the keywords (representation no. 5) as the original query, and representations 1-4 as expressions of query context. We choose to regard the keywords as the representation closest to original user query for two reasons. First, the keywords are the most similar to what a user might submit to a search engine, e.g. they are most often in the form of a few key terms or phrases, and are the least verbose of the five representations. Second, earlier results on the iSearch collection indicate \cite{Sorensen} that the single best performing representation among these five is the keywords, indicating that this representation works well with current retrieval models.

\subsubsection{Retrieval settings}
 We use the Indri IR system\footnote{http://www.lemurproject.org/indri/}. For ranking we use two versions of the language model: with Dirichlet (Dir) and Jelinek-Mercer (JM) smoothing \cite{Croft:2003}. We tune their parameters using the tuning range in Zhai and Lafferty (2002) \cite{ZhaiL:2002}:
 \begin{itemize}
 \item DIR's $\mu$ $\in$ \{100, 500, 800, 1000, 2000, 3000, 4000, 5000, 8000, 10000\}
 \item JM's $\lambda$ $\in$ \{0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99\}
 \end{itemize}
 We report the best retrieval performance separately for Mean Average Precision (MAP), Normalised Discounted Cumulated Gain (NDCG)\footnote{with the following gain values: very relevant = 3, fairly relevant = 2, marginally relevant = 1, non-relevant = 0.}, and Binary Preference (BPREF) in the top 1000 retrieved documents, and also for Precision at 10 (P@10), NDCG at 10 (NDCG@10) and Mean Reciprocal Rank (MRR). These measures contribute different aspects to the evaluation: Both MAP and BPREF measure the average precision of a ranked list, but BPREF differs from MAP because it does not treat non-assessed documents as explicitly non-relevant (whereas MAP does) \cite{BuckleyV04}. NDCG measures the gain of a document based on its position in the result list. The gain is accumulated from the top of the ranked list to the bottom, with the gain of each document discounted at lower ranks. This gain is relative to the ideal based on a known recall base of relevance assessments \cite{JarvelinK02}. P@10 and NDCG@10 focus on the early precision of the top 10 retrieved documents. MRR \cite{VoorheesT99} corresponds to the multiplicative inverse of the rank of the first relevant document retrieved, i.e. it focuses on the retrieval quality of the very top of the ranked list.

\subsection{Experimental Findings}
\label{ss:Experiments}
\def(90:0.5cm) circle (0.8cm){(90:0.5cm) circle (0.8cm)}
\def(210:0.5cm) circle (0.8cm){(210:0.5cm) circle (0.8cm)}
\def(330:0.5cm) circle (0.8cm){(330:0.5cm) circle (0.8cm)}
\begin{figure*}
\centering
\begin{tikzpicture}
\node[anchor=north,text width=14cm,text justified] () at (0,0)
   {\textbf{The user query ($Q$) and two representations ($A$ and $B$) of its context. Each representation contains positive and negative evidence about the strength of the combination $A \oplus B$ or $A \otimes B$ with respect to the query.}};
\end{tikzpicture}
\begin{minipage}[b]{0.45\linewidth}
\centering
\begin{tikzpicture}
\begin{scope}
\clip (330:0.5cm) circle (0.8cm);
\fill[cyan] (210:0.5cm) circle (0.8cm);
\end{scope}
\begin{scope}
\clip (90:0.5cm) circle (0.8cm);
\fill[cyan] (210:0.5cm) circle (0.8cm);
\end{scope}
\draw (90:0.5cm) circle (0.8cm) node[text=black,above] {\tiny Query $Q$};
\draw (210:0.5cm) circle (0.8cm) node [text=black,below left] {\tiny $A$};
\draw (330:0.5cm) circle (0.8cm) node [text=black,below right] {\tiny $B$};
\end{tikzpicture}
\caption{\label{fig:1}Positive evidence of $A$ (in $A \oplus B$).}
\end{minipage}
\hspace{0.5cm}
\begin{minipage}[b]{0.45\linewidth}
\centering
\begin{tikzpicture}
\begin{scope}
\clip (210:0.5cm) circle (0.8cm);
\fill[cyan] (330:0.5cm) circle (0.8cm);
\end{scope}
\begin{scope}
\clip (90:0.5cm) circle (0.8cm);
\fill[cyan] (330:0.5cm) circle (0.8cm);
\end{scope}
\draw (90:0.5cm) circle (0.8cm) node[text=black,above] {\tiny Query $Q$};
\draw (210:0.5cm) circle (0.8cm) node [text=black,below left] {\tiny $A$};
\draw (330:0.5cm) circle (0.8cm) node [text=black,below right] {\tiny $B$};
\end{tikzpicture}
\caption{\label{fig:2}Positive evidence of $B$ (in $A \oplus B$).}
\end{minipage}
\hspace{0.5cm}
\begin{minipage}[b]{0.45\linewidth}
\centering
\begin{tikzpicture}
\begin{scope}[shift={(1cm,0cm)}]
\begin{scope}[even odd rule
\clip (330:0.5cm) circle (0.8cm) (-1.3,-1.3) rectangle (1.3,1.3);
\clip (90:0.5cm) circle (0.8cm) (-1.3,-1.3) rectangle (1.3,1.3);
\fill[cyan] (210:0.5cm) circle (0.8cm);
\end{scope}
\draw (90:0.5cm) circle (0.8cm) node[text=black,above] {\tiny Query $Q$};
\draw (210:0.5cm) circle (0.8cm) node [text=black,below left] {\tiny $A$};
\draw (330:0.5cm) circle (0.8cm) node [text=black,below right] {\tiny $B$};
\end{scope}
\end{tikzpicture}
\caption{\label{fig:3}Negative evidence of $A$ (in $A \oplus B$).}
\end{minipage}
\hspace{0.5cm}
\begin{minipage}[b]{0.45\linewidth}
\centering
\begin{tikzpicture}
\begin{scope}[shift={(1cm,0cm)}]
\begin{scope}[even odd rule
\clip (210:0.5cm) circle (0.8cm) (-1.3,-1.3) rectangle (1.3,1.3);
\clip (90:0.5cm) circle (0.8cm) (-1.3,-1.3) rectangle (1.3,1.3);
\fill[cyan] (330:0.5cm) circle (0.8cm);
\end{scope}
\draw (90:0.5cm) circle (0.8cm) node[text=black,above] {\tiny Query $Q$};
\draw (210:0.5cm) circle (0.8cm) node [text=black,below left] {\tiny $A$};
\draw (330:0.5cm) circle (0.8cm) node [text=black,below right] {\tiny $B$};
\end{scope}
\end{tikzpicture}
\caption{\label{fig:4}Negative evidence of $B$ (in $A \oplus B$).}
\end{minipage}
\hspace{0.5cm}
\begin{minipage}[b]{0.45\linewidth}
\centering
\begin{tikzpicture}
\begin{scope}
\clip (330:0.5cm) circle (0.8cm);
\fill[cyan] (210:0.5cm) circle (0.8cm);
\end{scope}
\draw (90:0.5cm) circle (0.8cm) node[text=black,above] {\tiny Query $Q$};
\draw (210:0.5cm) circle (0.8cm) node [text=black,below left] {\tiny $A$};
\draw (330:0.5cm) circle (0.8cm) node [text=black,below right] {\tiny $B$};
\end{tikzpicture}
\caption{\label{fig:5}Positive evidence of $A$ about $B$ and also of $B$ about $A$ (in $A \otimes B$).}
\end{minipage}
\end{figure*}

\subsubsection{Mapping Opinions to Evidence}
\label{ss:EvidenceSpace}
According to the model of Lioma et al. (2010) summarised in section \ref{s:Model}, each representation of query context corresponds to an opinion about the original query. The belief, disbelief and uncertainty of these opinions must be computed from features of these query context representations (completely independently of the retrieval process). These features are referred to as evidence, and can be either positive or negative with respect to the original query, depending on whether they support it or not. Subjective logic maps this type of evidence to opinions as follows \cite{Josang:2001}. Let $r$ denote positive evidence, and $s$ denote negative evidence. Then, the correspondence between this evidence and the belief, disbelief, and uncertainty $b,d,u$ is:
\begin{equation}
\label{eq:mapping}
b = \frac{r}{r+s+2} \qquad d=\frac{s}{r+s+2} \qquad u = \frac{2}{r+s+2}
\end{equation}

What constitutes positive and negative evidence can be defined in various ways. In this work, we use the terms of the query context representations in a simple bag of words approach (regardless of their frequency, co-occurrence, grammatical or any other feature). We consider each query context representation and the original query as sets of terms. Then, positive evidence is the number of terms that occur in overlaps of these sets, and negative evidence is the number of terms that occur in complements of these sets, as described below.

\begin{table*}
\centering
\caption{\label{tab:polyrep}Pairwise polyrepresentation of query context representations through consensus ($\oplus$) and recommendation ($\otimes$) operations. Consensus and recommendation reflect the strength of each combination as a probability: the higher the probability, the more likely that the combination will benefit retrieval performance. Recommendation is non-commutative, so $A \otimes B$ and $B \otimes A$ denote the combination order.}
\begin{tabular}{l|c|c|c}
\multicolumn{4}{c}{$ $}\\
\hline
\multicolumn{4}{c}{POLYREPRESENTATION PROBABILITIES}\\
\hline
\multicolumn{4}{c}{(I) No Pre-Processing}\\
\hline
 \multirow{2}{*}{Query Context Representations}  &Consensus  &\multicolumn{2}{c}{Recommendation}\\
    &$A \oplus B$  &$A \otimes B$    &$B \otimes A$\\
\hline
user background - ideal answer      &0.1850             &0.4126             &0.4192\\
user background - work task       &0.1965             &0.3527             &0.4518\\
information need - user background  &0.1862             &0.3759             &0.4594\\
information need - ideal answer     &\bf0.2359          &0.3687             &0.4450\\
information need - work task      &0.2064             &0.3096             &\bf0.4764\\
work task - ideal answer          &0.1769             &\bf0.4258          &0.3859\\
\hline
\multicolumn{4}{c}{$ $}\\
\end{tabular}
\begin{tabular}{l|c|c|c}
\hline
\multicolumn{4}{c}{(II) Lower-Case, No Punctuation}\\
\hline
 \multirow{2}{*}{Query Context Representations}  &Consensus  &\multicolumn{2}{c}{Recommendation}\\
    &$A \oplus B$  &$A \otimes B$    &$B \otimes A$\\
\hline
user background - ideal answer      &0.2285             &0.3967             &0.4153\\
user background - work task       &0.2347             &0.3319             &0.4540\\
information need - user background  &0.2426             &0.3470             &0.4702\\
information need - ideal answer     &\bf0.2939          &0.3379             &0.4584\\
information need - work task      &0.2534             &0.2680             &\bf0.4895\\
work task - ideal answer          &0.2123             &\bf0.4137          &0.3811\\
\hline
\multicolumn{4}{c}{$ $}\\
\end{tabular}
\begin{tabular}{l|c|c|c}
\hline
\multicolumn{4}{c}{(III) Lower-Case, No Punctuation, No Stopwords}\\
\hline
 \multirow{2}{*}{Query Context Representations}  &Consensus  &\multicolumn{2}{c}{Recommendation}\\
    &$A \oplus B$  &$A \otimes B$    &$B \otimes A$\\
\hline
user background - ideal answer      &0.1972             &0.4365             &0.4499\\
user background - work task       &0.1826             &0.4030             &0.4641\\
information need - user background  &0.2457             &0.3928             &0.4787\\
information need - ideal answer     &\bf0.3154          &0.3493             &0.4734\\
information need - work task      &0.2715             &0.2991             &\bf0.4907\\
work task - ideal answer          &0.1902             &\bf0.4436          &0.4274\\
\hline
\multicolumn{4}{c}{$ $}\\
\end{tabular}
\begin{tabular}{l|c|c|c}
\hline
\multicolumn{4}{c}{(IV) Lower-Case, No Punctuation, No Stopwords, Stemming}\\
\hline
 \multirow{2}{*}{Query Context Representations}  &Consensus  &\multicolumn{2}{c}{Recommendation}\\
    &$A \oplus B$  &$A \otimes B$    &$B \otimes A$\\
\hline
user background - ideal answer        &0.2278             &0.4211             &0.4424\\
user background - work task         &0.2203             &0.3771             &0.4616\\
information need - user background    &0.2885             &0.3607             &0.4837\\
information need - ideal answer       &\bf0.3674          &0.3138             &0.4868\\
information need - work task        &0.3106             &0.2615             &\bf0.5027\\
work task - ideal answer            &0.2172             &\bf0.4307          &0.4170\\
\hline
\multicolumn{4}{c}{$ $}\\
\end{tabular}
\end{table*}

For the consensus of two query context representations $A$ and $B$ ($A \oplus B$) with respect to a user query $Q$, $A$'s positive evidence is the number of terms occurring in $A \cap B$ and also in $A \cap Q$ (Figure \ref{fig:1}). Similarly, $B$'s positive evidence is the number of terms occurring in $A \cap B$ and also in $B \cap Q$ (Figure \ref{fig:2}). $A$'s negative evidence is the number of terms occurring in the complement of $A - B$ and $A - Q$ (Figure \ref{fig:3}). $B$'s negative evidence is the number of terms occurring in the complement of $B - A$ and $B - Q$ (Figure \ref{fig:4}).

For the recommendation of $A$ to $B$ and of $B$ to $A$ ($A \otimes B$), both $A$'s and $B$'s positive evidence is the number of terms occurring in $A \cap B$ (Figure \ref{fig:5}). The negative evidence of $A$ and $B$ is the same as in the consensus above.

We apply these separate pre-processing options when counting terms:
\begin{itemize}
\item[](I) no-preprocessing at all
\item[](II) lower-case, punctuation removal
\item[](III) II + stopword removal
\item[](IV) III + stemming
\end{itemize}
We use the SMART stopword list, as appears in the appendices of Lewis et al. (2004) \cite{LewisYRL04}, and the Porter stemming algorithm \cite{Porter80}.

Any type and amount of contextual information that can potentially be useful may be used as evidence, for instance statistical, linguistic, algorithmic or other features of the queries (e.g. see \cite{LiomaB:2009} for pragmatic query features). Additional evidence can be modelled by introducing more opinions.

Having collected this evidence from the term statistics of the query context representations, we compute their combinations by consensus (using Equations \ref{eq:consensus1} - \ref{eq:consensus2}) and recommendation (using Equations \ref{eq:recommendation1} - \ref{eq:recommendation2}). The output of these equations is fused beliefs, disbeliefs, and uncertainties for each combination, which we feed into Equation \ref{eq:expectation} to compute the final strength of each combination - we refer to this as \textit{polyrepresentation probability}. In Equation \ref{eq:expectation}, we set the prior $\alpha = 0.5$ assuming a binary frame of discernment (i.e. having two states and dividing $\alpha$ uniformally across these).

\subsubsection{Polyrepresentation Findings}
\label{ss:Results1}

Table \ref{tab:polyrep} shows the probabilities of each combination of query context representations computed as described above. The three query context representations that give higher polyrepresentation probabilities consistently for all preprocessing options are the user's information need, work task and ideal answer. Among these, work task and information need give the highest polyrepresentation probability at all times, using the recommendation operation (in this order). The assumption behind this combination is that (a) work task and information need are dependent, and that (b) the work task is a better contextual representation of the information need than of the original query. This can be seen in the very low probability fetched by the recommendation information need $\otimes$ work task (which is the lowest probability for all recommendation combinations). Simply speaking, what we see here is two query context representations that are potentially valuable to retrieval effectiveness but only if their effect is channelled in a specific way so that their effect is traced according to their dependence. Their dependence in this case is that the work task context supports the information need, and the information need context supports the query. This is in line with the cognitive interpretation of user's work task in interactive IR laid down in Ingwersen (1996) \cite{Ingwersen:1996}.

Table \ref{tab:polyrep} also shows that recommendation results in higher overall polyrepresentation probabilities than consensus, i.e. combinations that are likely to be more reliable. To assess the predictions of the query context combinations in Table \ref{tab:polyrep}, we compare them to the actual retrieval performance of each combination, described next. No part of the retrieval process has been informed by the polyrepresentation computation described above, and vice versa.

\begin{table*}
\centering
\caption{\label{tab:scores}Retrieval performance using the language model with Dirichlet (DIR) and Jelinek-Mercer (JM) smoothing. The best combination of query context representations is shown in bold. $\dagger$ marks the best overall score per evaluation measure.}
\scalebox{1}{
\begin{tabular}{l|cc|cc|cc}
\hline
\multirow{2}{*}{Query Context Representations}    &\multicolumn{2}{c|}{MAP}    &\multicolumn{2}{|c|}{NDCG}   &\multicolumn{2}{|c}{BPREF}\\
&DIR&JM&DIR&JM&DIR&JM\\
\hline
user background - ideal answer      &0.0588&0.0703  &0.1928&0.2217  &0.2042&0.2347  \\
user background - work task       &0.0933&0.1095  &0.2589&0.3062  &0.2737&0.3059  \\
information need - user background  &0.1073&0.1229  &0.2919&0.3341  &0.2844&0.3213  \\
information need - ideal answer     &0.0945&0.1077  &0.2670&0.2983  &0.2508&0.2834  \\
information need - work task      &\bf0.1175&\bf0.1310$\dagger$  &\bf0.3117&\bf0.3563  &\bf0.2974&\bf0.3270  \\
work task - ideal answer          &0.0849&0.0925  &0.2489&0.2895  &0.2480&0.2960  \\
\hline
original query (no context)           &0.1156&0.1268  &0.3339&0.3572$\dagger$  &0.3201&0.3340$\dagger$\\
\hline
\multicolumn{4}{c}{$ $}\\
\end{tabular}
}
\scalebox{1}{
\begin{tabular}{l|cc|cc|cc}
\hline
\multirow{2}{*}{Query Context Representations}    &\multicolumn{2}{c|}{P@10} &\multicolumn{2}{|c|}{NDCG@10}    &\multicolumn{2}{|c}{MRR}\\
&DIR&JM&DIR&JM&DIR&JM\\
\hline
user background - ideal answer      &0.1446&0.1477  &0.1298&0.1314  &0.3069&0.3310\\
user background - work task       &0.2077&0.2277  &0.1911&0.2061  &0.3884&0.3907\\
information need - user background  &0.2154&0.2138  &0.2004&0.2075  &0.4222&0.4600\\
information need - ideal answer     &0.2185&0.2154  &0.1976&0.1919  &0.4394&0.4167\\
information need - work task      &\bf0.2523&\bf0.2554$\dagger$  &\bf0.2246&\bf0.2352$\dagger$  &\bf0.4582&\bf0.4880\\
work task - ideal answer          &0.2000&0.1892  &0.1883&0.1805  &0.4134&0.3997\\
\hline
original query (no context)           &0.2492&0.2431  &0.2287&0.2175  &0.5267$\dagger$&0.4958\\
\hline
\end{tabular}
}
\end{table*}

\begin{figure*}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=,

			xlabel=Polyrepresentation Probability,
			ylabel=MAP

			]
			\pgfplotstableread{polyrep-ret.data}\table
			\addplot[mark=+] table[x index=0,y index=1] from \table;
\end{axis}
\end{tikzpicture}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=,

			xlabel=Polyrepresentation Probability,
			ylabel=NDCG

			]
			\pgfplotstableread{polyrep-ret.data}\table
			\addplot[mark=+] table[x index=0,y index=2] from \table;
\end{axis}
\end{tikzpicture}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=,

			xlabel=Polyrepresentation Probability,
			ylabel=BPREF

			]
			\pgfplotstableread{polyrep-ret.data}\table
			\addplot[mark=+] table[x index=0,y index=3] from \table;
\end{axis}
\end{tikzpicture}
\begin{tikzpicture}
\node[anchor=north,text width=16.5cm] () at (0,0)
   {\textbf{$\qquad$}};
\end{tikzpicture}
\begin{tikzpicture}
\node[anchor=north,text width=16.5cm] () at (0,0)
   {\textbf{$\qquad$}};
\end{tikzpicture}
\begin{tikzpicture}
\node[anchor=north,text width=16.5cm] () at (0,0)
   {\textbf{$\qquad$}};
\end{tikzpicture}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=,

			xlabel=Polyrepresentation Probability,
			ylabel=P@10

			]
			\pgfplotstableread{polyrep-ret.data}\table
			\addplot[mark=+] table[x index=0,y index=4] from \table;
\end{axis}
\end{tikzpicture}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=,

			xlabel=Polyrepresentation Probability,
			ylabel=NDCG@10

			]
			\pgfplotstableread{polyrep-ret.data}\table
			\addplot[mark=+] table[x index=0,y index=5] from \table;
\end{axis}
\end{tikzpicture}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=,

			xlabel=Polyrepresentation Probability,
			ylabel=MRR

			]
			\pgfplotstableread{polyrep-ret.data}\table
			\addplot[mark=+] table[x index=0,y index=6] from \table;
\end{axis}
\end{tikzpicture}
\caption{\label{fig:comparison}Probability of each polyrepresentation combination (x axis) vs. retrieval performance with JM (y axis) for all query context representation combinations (the points correspond to the scores in Tables \ref{tab:polyrep} \& \ref{tab:scores}).}
\end{figure*}

\subsubsection{Retrieval Findings}
Table \ref{tab:scores} shows the retrieval performance of the combinations of query context representations we saw in Table \ref{tab:polyrep}. For these runs, we use the text of each query context representation as query text, and the same four types of preprocessing reported in Section \ref{ss:EvidenceSpace}. We do not  weight separately any of the query fields; we simply concatenate all text into one query. Table \ref{tab:scores} also displays the retrieval performance when using the original query without any context, for reference (there is no polyrepresentation of context in this run). We see that the combination with the highest polyrepresentation probability in Table \ref{tab:polyrep}, information need and work task, gives also the best retrieval performance among all combinations. This is consistent for all six evaluation measures, and for both retrieval models. Comparing these results to the performance of the original query without context, we see that the best polyrepresentation run improves retrieval performance over the original query for MAP, P@10 and NDCG@10, and is comparable to the original query for the remaining evaluation measures. Overall, these findings indicate that the polyrepresentation probabilities tend to agree with observed retrieval performance. This agreement is also visually displayed in Figure \ref{fig:comparison}. We see that there is overall consistency among the two, apart from the case of background - work task.

The scores shown in Table \ref{tab:scores} are averaged over all queries, meaning that they can be affected by outliers. Figures \ref{fig:perquery1} - \ref{fig:perquery2} present a detailed per-query overview of the retrieval performance of each query (measured in MAP only for DIR) against the belief and uncertainty of the query context representations combined with consensus and recommendation\footnote{For the recommendation, we present one order of combination (out of the two); we can report that the other combination order follows very similar trends.}. This belief and uncertainty are the ingredients used to compute the polyrepresentation probabilities with Equation \ref{eq:expectation}. Hence, these figures serve to explain how the polyrepresentatin probabilities can be decomposed and how their components correlate to retrieval performance. The correlation between MAP and the respective belief and uncertainty values is reported using Spearman's rank correlation coefficient. We can report similar trends for runs with JM and for the other evaluation measures that average retrieval precision in the top 1000 documents.

Figures \ref{fig:perquery1} - \ref{fig:perquery2} show that the combination of information need and work task (which gave the highest polyrepresentation probability and also the highest retrieval performance) does not have the highest correlation with MAP on a per query basis, neither for belief nor for uncertainty. The highest correlation with respect to these is given by the combination of information need with user background. However, the combination of information need and work task has overall the highest belief values among all combinations. This shows that this combination has somewhat stronger evidence than the others, which may not always correlate strongly with the MAP score flunctuations for each query, but which nevertheless is an overall better representation of the query context.

\begin{figure*}
\centering
\begin{tikzpicture}
\node[anchor=north,text width=14cm,text justified] () at (0,0)
   {\textbf{The belief and uncertainty (y axis) of the representations used for the consensus combination (this page) and the recommendation operator (next page) against MAP (x axis) per query, and their rank correlation coefficients (Spearman's $\rho$). The order of the combination for recommendation is shown in the title of each figure.}};
\end{tikzpicture}
\centering
\pgfplotsset{every axis label/.append style={font=\large}}
\tikzset{every mark/.append style={font=\large}}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Background $\oplus$ Ideal Answer,

			xlabel=MAP,
			ylabel=Belief,
			yticklabels={0.002,0.1},
			ytick={0.002,0.1}

			]
			\pgfplotstableread{BK-IA-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=4] from \table;
\end{axis}
\end{tikzpicture}
\caption{\label{fig:perquery1}$\rho=0.123$}
\end{minipage}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Background $\oplus$ Task,

			xlabel=MAP,
			ylabel=Belief,
			yticklabels={0.01,0.1},
			ytick={0.005,0.1}

			]
			\pgfplotstableread{BK-WT-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=4] from \table;
\end{axis}
\end{tikzpicture}
\caption{$\rho=-0.014$}
\end{minipage}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Information need $\oplus$ Background,

			xlabel=MAP,
			ylabel=Belief,
			yticklabels={0.09,0.1},
			ytick={0.009,0.1}

			]
			\pgfplotstableread{IN-BK-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=4] from \table;
\end{axis}
\end{tikzpicture}
\caption{$\rho=0.483$}
\end{minipage}
\begin{tikzpicture}
\node[anchor=north,text width=16.5cm] () at (0,0)
   {\textbf{$\qquad$}};
\end{tikzpicture}
\begin{tikzpicture}
\node[anchor=north,text width=16.5cm] () at (0,0)
   {\textbf{$\qquad$}};
\end{tikzpicture}
\begin{tikzpicture}
\node[anchor=north,text width=16.5cm] () at (0,0)
   {\textbf{$\qquad$}};
\end{tikzpicture}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Information Need $\oplus$ Ideal Answer,

			xlabel=MAP,
			ylabel=Belief,
			yticklabels={0.02,0.1},
			ytick={0.002,0.1},
			xtick={0.0005,0.02},
			xticklabels={0.0005,0.02}
			]
			\pgfplotstableread{IN-IA-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=4] from \table;
\end{axis}
\end{tikzpicture}
\caption{$\rho=0.411$}
\end{minipage}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Information Need $\oplus$ Task,

			xlabel=MAP,
			ylabel=Belief,
			yticklabels={0.2,0.05},
			ytick={0.05,0.2}

			]
			\pgfplotstableread{IN-WT-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=4] from \table;
\end{axis}
\end{tikzpicture}
\caption{$\rho=0.359$}
\end{minipage}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Task $\oplus$ Ideal Answer,

			xlabel=MAP,
			ylabel=Belief,
			yticklabels={0.02,0.005},
			ytick={0.02,0.005}

			]
			\pgfplotstableread{WT-IA-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=4] from \table;
\end{axis}
\end{tikzpicture}
\caption{$\rho=0.049$}
\end{minipage}
\begin{tikzpicture}
\node[anchor=north,text width=16.5cm] () at (0,0)
   {\textbf{$\qquad$}};
\end{tikzpicture}
\begin{tikzpicture}
\node[anchor=north,text width=16.5cm] () at (0,0)
   {\textbf{$\qquad$}};
\end{tikzpicture}
\begin{tikzpicture}
\node[anchor=north,text width=16.5cm] () at (0,0)
   {\textbf{$\qquad$}};
\end{tikzpicture}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Background $\oplus$ Ideal Answer,

			xlabel=MAP,
			ylabel=Uncertainty

			]
			\pgfplotstableread{BK-IA-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=5] from \table;
\end{axis}
\end{tikzpicture}
\caption{$\rho=-0.009$}
\end{minipage}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Background $\oplus$ Task,

			xlabel=MAP,
			ylabel=Uncertainty

			]
			\pgfplotstableread{BK-WT-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=5] from \table;
\end{axis}
\end{tikzpicture}
\caption{$\rho=-0.198$}
\end{minipage}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Information need $\oplus$ Background,

			xlabel=MAP,
			ylabel=Uncertainty

			]
			\pgfplotstableread{IN-BK-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=5] from \table;
\end{axis}
\end{tikzpicture}
\caption{$\rho=-0.209$}

\end{minipage}
\begin{tikzpicture}
\node[anchor=north,text width=16.5cm] () at (0,0)
   {\textbf{$\qquad$}};
\end{tikzpicture}
\begin{tikzpicture}
\node[anchor=north,text width=16.5cm] () at (0,0)
   {\textbf{$\qquad$}};
\end{tikzpicture}
\begin{tikzpicture}
\node[anchor=north,text width=16.5cm] () at (0,0)
   {\textbf{$\qquad$}};
\end{tikzpicture}

\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Information Need $\oplus$ Ideal Answer,

			xlabel=MAP,
			ylabel=Uncertainty,

			xtick={0.0005,0.02},
			xticklabels={0.0005,0.02}
			]
			\pgfplotstableread{IN-IA-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=5] from \table;
\end{axis}
\end{tikzpicture}
\caption{$\rho=-0.146$}
\end{minipage}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Information Need $\oplus$ Task,

			xlabel=MAP,
			ylabel=Uncertainty

			]
			\pgfplotstableread{IN-WT-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=5] from \table;
\end{axis}
\end{tikzpicture}
\caption{$\rho=-0.065$}
\end{minipage}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Task $\oplus$ Ideal Answer,

			xlabel=MAP,
			ylabel=Uncertainty

			]
			\pgfplotstableread{WT-IA-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=5] from \table;
\end{axis}
\end{tikzpicture}
\caption{$\rho=-0-007$}
\end{minipage}
\label{fig:perquery}
\end{figure*}
\begin{figure*}
\centering
\pgfplotsset{every axis label/.append style={font=\large}}
\tikzset{every mark/.append style={font=\large}}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Ideal Answer $\otimes$ Background,

			xlabel=MAP,
			ylabel=Belief,
			yticklabels={0.002,0.1},
			ytick={0.002,0.1}

			]
			\pgfplotstableread{BK-IA-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=6] from \table;
\end{axis}
\end{tikzpicture}
\caption{$\rho=0.062$}
\end{minipage}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Task $\otimes$ Background,

			xlabel=MAP,
			ylabel=Belief,
			yticklabels={0.01,0.1},
			ytick={0.005,0.1}

			]
			\pgfplotstableread{BK-WT-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=6] from \table;
\end{axis}
\end{tikzpicture}
\caption{$\rho=0.044$}
\end{minipage}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Background $\otimes$ Information Need,

			xlabel=MAP,
			ylabel=Belief,
			yticklabels={0.09,0.1},
			ytick={0.009,0.1}

			]
			\pgfplotstableread{IN-BK-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=6] from \table;
\end{axis}IN-BK-DIR-MAP
\end{tikzpicture}
\caption{$\rho=0.238$}
\end{minipage}
\begin{tikzpicture}
\node[anchor=north,text width=16.5cm] () at (0,0)
   {\textbf{$\qquad$}};
\end{tikzpicture}
\begin{tikzpicture}
\node[anchor=north,text width=16.5cm] () at (0,0)
   {\textbf{$\qquad$}};
\end{tikzpicture}
\begin{tikzpicture}
\node[anchor=north,text width=16.5cm] () at (0,0)
   {\textbf{$\qquad$}};
\end{tikzpicture}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Ideal $\otimes$ Information Need,

			xlabel=MAP,
			ylabel=Belief,
			yticklabels={0.02,0.1},
			ytick={0.002,0.1}

			]
			\pgfplotstableread{IN-IA-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=6] from \table;
\end{axis}
\end{tikzpicture}
\caption{$\rho=0.086$}
\end{minipage}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Task $\otimes$ Information Need,

			xlabel=MAP,
			ylabel=Belief,
			yticklabels={0.05,0.15},
			ytick={0.05,0.15}

			]
			\pgfplotstableread{IN-WT-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=6] from \table;
\end{axis}
\end{tikzpicture}
\caption{$\rho=0.117$}
\end{minipage}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Ideal Answer $\otimes$ Task,

			xlabel=MAP,
			ylabel=Belief,
			yticklabels={0.02,0.1},
			ytick={0.02,0.1}

			]
			\pgfplotstableread{WT-IA-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=6] from \table;
\end{axis}
\end{tikzpicture}
\caption{$\rho=0.105$}
\end{minipage}
\begin{tikzpicture}
\node[anchor=north,text width=16.5cm] () at (0,0)
   {\textbf{$\qquad$}};
\end{tikzpicture}
\begin{tikzpicture}
\node[anchor=north,text width=16.5cm] () at (0,0)
   {\textbf{$\qquad$}};
\end{tikzpicture}
\begin{tikzpicture}
\node[anchor=north,text width=16.5cm] () at (0,0)
   {\textbf{$\qquad$}};
\end{tikzpicture}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Ideal Answer $\otimes$ Background,

			xlabel=MAP,
			ylabel=Uncertainty

			]
			\pgfplotstableread{BK-IA-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=7] from \table;
\end{axis}
\end{tikzpicture}
\caption{$\rho=-0.080$}
\end{minipage}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Task $\otimes$ Background,

			xlabel=MAP,
			ylabel=Uncertainty

			]
			\pgfplotstableread{BK-WT-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=7] from \table;
\end{axis}
\end{tikzpicture}
\caption{$\rho=0.019$}
\end{minipage}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Background $\otimes$ Information Need,

			xlabel=MAP,
			ylabel=Uncertainty

			]
			\pgfplotstableread{IN-BK-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=7] from \table;
\end{axis}
\end{tikzpicture}
\caption{$\rho=-0.136$}

\end{minipage}
\begin{tikzpicture}
\node[anchor=north,text width=16.5cm] () at (0,0)
   {\textbf{$\qquad$}};
\end{tikzpicture}
\begin{tikzpicture}
\node[anchor=north,text width=16.5cm] () at (0,0)
   {\textbf{$\qquad$}};
\end{tikzpicture}
\begin{tikzpicture}
\node[anchor=north,text width=16.5cm] () at (0,0)
   {\textbf{$\qquad$}};
\end{tikzpicture}

\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Ideal $\otimes$ Information Need,

			xlabel=MAP,
			ylabel=Uncertainty

			]
			\pgfplotstableread{IN-IA-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=7] from \table;
\end{axis}
\end{tikzpicture}
\caption{$\rho=-0.064$}
\end{minipage}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Task $\otimes$ Information Need,

			xlabel=MAP,
			ylabel=Uncertainty

			]
			\pgfplotstableread{IN-WT-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=7] from \table;
\end{axis}
\end{tikzpicture}
\caption{$\rho=-0.061$}
\end{minipage}
\begin{minipage}[b]{0.3\linewidth}
\centering
\begin{tikzpicture}[baseline,scale=0.55]
			\begin{axis}[
			font=\large,
			title=Ideal Answer $\otimes$ Task,

			xlabel=MAP,
			ylabel=Uncertainty

			]
			\pgfplotstableread{WT-IA-DIR-MAP}\table
			\addplot[only marks,mark=+] table[x index=2,y index=7] from \table;
\end{axis}
\end{tikzpicture}
\caption{\label{fig:perquery2}$\rho=-0.136$}
\end{minipage}
\label{fig:perquery}
\end{figure*}

\section{Discussion}
\label{s:Discussion}
The experimental findings presented above can be summarised in three points.

The experiments indicate that the polyrepresentation predictions regarding the optimal combination of query context representations were in agreement with observed retrieval performance. This validates the model of Lioma et al. (2010) for this dataset and retrieval scenario. Further experiments with other settings and using more than pairwise combinations of representations are needed to ground the model on firmer grounds - we consider this study as a first step in that direction.

We showed how the components of each polyrepresentation combination were induced from naive bag of words term statistics and converted to belief, disbelief and uncertainty. This was a straight-forward application of the mapping proposed in J\o sang (2001) \cite{Josang:2001} and of features common to IR experimentation. Further features could be used, potentially challenging their mapping to binary (positive or  negative) evidence, resulting in new definitions and mappings of graded features as contextual evidence for IR.

Finally, we showed how the effect of one representation upon the other can be traced and modelled in a combination. This is an attractive feature that could be used to model and practically adjust the user retrieval experience, through for instance interface functionalities, such as timely interventions or suggestions.

\section{Conclusions}
\label{s:Conclusions}

This work provided a practical application and analysis of the principle of polyrepresentation formalised using subjective logic, as initially proposed in Lioma et al. (2010). We showed how to map the abstract notions of belief and uncertainty used in that model to real-life evidence drawn from a retrieval test collection, and how to estimate two different types of combinations for polyrepresentation assuming either (a) independence or (b) dependence between the information objects that are combined. Experimental evidence on the polyrepresentation of different types of context relating to user information needs (i.e. work task, user background knowledge, ideal answer) using two state of the art retrieval models, six standand evaluation measures and 65 queries showed that the model of Lioma et al. (2010) can predict their optimal combination prior and independently to the retrieval process.

\section{Acknowledgments}
We thank the anonymous reviewers for useful feedback.
\bibliographystyle{abbrv}

redpajama_set_name: RedPajamaArXiv


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John Henry Williams, VC, DCM, MM & Bar (29 September 1886 – 7 March 1953) was a Welsh colliery worker, soldier, and a recipient of the Victoria Cross, the highest award for gallantry in the face of the enemy that can be awarded to British and Commonwealth forces. Williams is the most decorated Welsh non-commissioned officer of all time.
Williams was born in Nantyglo, Monmouthshire, on 29 September 1886.
In November 1914, Williams gave up his employment as a colliery blacksmith and enlisted in the 10th Battalion, South Wales Borderers (part of the 38th (Welsh) Division). He was promoted to sergeant in January 1915.
For most conspicuous bravery, initiative and devotion to duty on the night of 7th – 8th October 1918, during the attack on Villers Outreaux, when, observing that his company was suffering heavy casualties from an enemy machine gun, he ordered a Lewis Gun to engage it, and went forward, under heavy fire, to the flank of the enemy post which he rushed single handed, capturing fifteen of the enemy.
These prisoners, realising that Williams was alone, turned on him and one of them gripped his rifle. He succeeded in breaking away and bayonetting five enemy, whereupon the remainder again surrendered. By this gallant action and total disregard of personal danger, he was the means of enabling not only his own company but also those on the flanks to advance.
Company sergeant major Williams was medically discharged from the army on 17 October 1918 after being severely wounded by shrapnel in the right arm and leg.
In 1919, Williams was invested with his Victoria Cross, Distinguished Conduct Medal, Military Medal and Bar by King George V, the first time that the King had decorated the same man four times in one day. At the time of the investiture Williams had not recovered from his severe wounds, and during the presentation the wound in his arm opened up with the result that medical attention had to be given before he could leave the palace.
Williams' grave and memorial are at Ebbw Vale Cemetery. The original headstone was removed during cemetery clearance and a new headstone was erected on 21 October 1990.
In September 2018 the villagers of Villers-Outréaux dedicated a especially commissioned memorial to commemorate him and express their thanks for saving their village from certain destruction.
Williams' Victoria Cross is displayed at the Regimental Museum of The Royal Welsh, Brecon.
^ "No. 31067". The London Gazette (Supplement). 13 December 1918. p. 14776.
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