OpenRaiser/Intern-Atlas huggingface dataset:
Enabling accent resilient speech based information retrieval
Authors: Koushik Sinha, Geetha Manjunath, Raveesh R. Sharma, Viswanath Gangavaram, Pooja A, Deepak R. Murugaian
Venue: WWW
Year: 2012

Voice interfaces to browsers and mobile applications are becoming popular as typing with touch screens is cumbersome. The main issue of practical speech based interfaces is how to overcome speech recognition errors. This problem is more severe when the users are non-native speakers of English due to differences in pronunciations. In this paper, we describe a novel, intelligent speech interface design approach for IR tasks that is significantly robust to accent variations. Our solution uses phonemic similarity based word spreading and semantic information based filtering to boost the accuracy of any ASR. We evaluated our solution with Google Voice as the ASR for a web question-answering system developed in-house and the results are very encouraging.
Dynamical information retrieval modelling: a portfolio-armed bandit machine approach
Authors: Marc Sloan, Jun Wang
Venue: WWW
Year: 2012

The dynamic nature of document relevance is largely ignored by traditional Information Retrieval (IR) models, which assume that scores (relevance) for documents given an information need are static. In this paper, we formulate a general Dynamical Information Retrieval problem, where we consider retrieval as a stochastic, controllable process. The ranking action continuously controls the retrieval system's dynamics and an optimal ranking policy is found that maximizes the overall users' satisfaction during each period. Through deriving the posterior probability of the documents evolving relevancy from user clicks, we can provide a plug-in framework for incorporating a number of click models, which can be combined with Multi-Armed Bandit theory and Portfolio Theory of IR to create a dynamic ranking rule that takes rank bias and click dependency into account. We verify the versatility of our algorithms in a number of experiments and demonstrate improved performance over strong baselines and as a result significant performance gains have been achieved.
Multimedia search over integrated social and sensor networks
Authors: John Soldatos, Moez Draief, Craig Macdonald, Iadh Ounis
Venue: WWW
Year: 2012

This paper presents work in progress within the FP7 EU-funded project SMART to develop a multimedia search engine over content and information stemming from the physical world, as derived through visual, acoustic and other sensors. Among the unique features of the search engine is its ability to respond to social queries, through integrating social networks with sensor networks. Motivated by this innovation, the paper presents and discusses the state-of-the-art in participatory sensing and other technologies blending social and sensor networks.
Detecting dynamic association among twitter topics
Authors: Shuangyong Song, Qiudan Li, Hongyun Bao
Venue: WWW
Year: 2012

Over the last few years, Twitter is increasingly becoming an important source of up-to-date topics about what is happening in the world. In this paper, we propose a dynamic topic association detection model to discover relations between Twitter topics, by which users can gain insights into richer information about topics of interest. The proposed model utilizes a time constrained method to extract event-based spatio-temporal topic association, and constructs a dynamic temporal map to represent the obtained result. Experimental results show the improvement of the proposed model compared to static spatio-temporal method and co-occurrence method.
Business session "social media and news
Authors: Jochen Spangenberg
Venue: WWW
Year: 2012

The workshop also includes a business section that will focus on aspects of Social Media in the News domain. Panelists with expertise in innovation management, news provision, journalism and market developments will discuss some of the challenges of and opportunities for the news sector with regards to Social Media. This part of the workshop is organized and brought to you by the SocialSensor project.
Tracking entities in web archives: the LAWA project
Authors: Marc Spaniol, Gerhard Weikum
Venue: WWW
Year: 2012

Web-preservation organization like the Internet Archive not only capture the history of born-digital content but also reflect the zeitgeist of different time periods over more than a decade. This longitudinal data is a potential gold mine for researchers like sociologists, politologists, media and market analysts, or experts on intellectual property. The LAWA project (Longitudinal Analytics of Web Archive data) is developing an Internet-based experimental testbed for large-scale data analytics on Web archive collections. Its emphasis is on scalable methods for this specific kind of big-data analytics, and software tools for aggregating, querying, mining, and analyzing Web contents over long epochs. In this paper, we highlight our research on {\em entity-level analytics} in Web archive data, which lifts Web analytics from plain text to the entity-level by detecting named entities, resolving ambiguous names, extracting temporal facts and visualizing entities over extended time periods. Our results provide key assets for tracking named entities in the evolving Web, news, and social media.
Open and decentralized platform for visualizing web mash-ups in augmented and mirror worlds
Authors: Vlad Stirbu, David Murphy, Yu You
Venue: WWW
Year: 2012

Augmented reality applications are gaining popularity due to increased capabilities of modern mobile devices. However, existing applications are tightly integrated with backend services that expose content using proprietary interfaces. We demonstrate an architecture that allows visualization of web content in augmented and mirror world applications, based on open web protocols and formats. We describe two clients, one for creating virtual artifacts, web resources that bind together web content with location and a 3D model, and one that visualizes the virtual artifacts in the mirror world.
The use of social media within the global disaster alert and coordination system (GDACS)
Authors: Beate Stollberg, Tom De Groeve
Venue: WWW
Year: 2012

The Global Disaster Alert and Coordination System (GDACS) collects near real-time hazard information to provide global multi-hazard disaster alerting for earthquakes, tsunamis, tropical cyclones, floods and volcanoes. GDACS alerts are based on calculations from physical disaster parameters and used by emergency responders. In 2011, the Joint Research Centre (JRC) of the European Commission started exploring if and how social media could be an additional valuable data source for international disaster response. The question is if awareness of the situation after a disaster could be improved by the use of social media tools and data. In order to explore this, JRC developed a Twitter account and Facebook page for the dissemination of GDACS alerts, a Twitter parser for the monitoring of information and a mobile application for information exchange. This paper presents the Twitter parser and the intermediate results of the data analysis which shows that the parsing of Twitter feeds (so-called tweets) can provide important information about side effects of disasters, on the perceived impact of a hazard and on the reaction of the affected population. The most important result is that impact information on collapsed buildings were detected through tweets within the first half an hour after an earthquake occurred and before any mass media reported the collapse.
Turning a Web 2.0 social network into a Web 3.0, distributed, and secured social web application
Authors: Henry Story, Romain Blin, Julien Subercaze, Christophe Gravier, Pierre Maret
Venue: WWW
Year: 2012

This demonstration presents the process of transforming a Web 2.0 centralized social network into a Web 3.0, distributed, and secured Social application, and what was learnt in this process. The initial Web 2.0 Social Network application was written by a group of students over a period of 4 months in the spring of 2011. It had all the bells and whistles of the well known Social Networks: walls to post on, circles of friends, etc. The students were very enthusiastic in building their social network, but the chances of it growing into a large community were close to non-existent unless a way could be found to tie it into a bigger social network. This is where linked data protected by the Web Access Control Ontology and WebID authentication could come to the rescue. The paper describes this transformation process, and we will demonstrate the full software version at the conference.
Actualization of query suggestions using query logs
Authors: Alisa Strizhevskaya, Alexey Baytin, Irina Galinskaya, Pavel Serdyukov
Venue: WWW
Year: 2012

In this work we are studying actualization techniques for building an up-to-date query suggestions model using query logs. The performance of the proposed actualization algorithms was estimated by real query flow of the Yandex search engine.
Identification of top relevant temporal expressions in documents
Authors: Jannik Strötgen, Omar Alonso, Michael Gertz
Venue: WWW
Year: 2012

Temporal information is very common in textual documents, and thus, identifying, normalizing, and organizing temporal expressions is an important task in IR. Although there are some tools for temporal tagging, there is a lack in research focusing on the relevance of temporal expressions. Besides counting their frequency and verifying whether they satisfy a temporal search query, temporal expressions are often considered in isolation only. There are no methods to calculate the relevance of temporal expressions, neither in general nor with respect to a query.
Adding fake facts to ontologies
Authors: Fabian M. Suchanek, David Gross-Amblard
Venue: WWW
Year: 2012

In this paper, we study how artificial facts can be added to an RDFS ontology. Artificial facts are an easy way of proving the ownership of an ontology: If another ontology contains the artificial fact, it has probably been taken from the original ontology. We show how the ownership of an ontology can be established with provably tight probability bounds, even if only parts of the ontology are being re-used. We explain how artificial facts can be generated in an inconspicuous and minimally disruptive way. Our demo allows users to generate artificial facts and to guess which facts were generated.
Query spelling correction using multi-task learning
Authors: Xu Sun, Anshumali Shrivastava, Ping Li
Venue: WWW
Year: 2012

This paper explores the use of online multi-task learning for search query spelling correction, by effectively transferring information from different and biased training datasets for improving spelling correction across datasets. Experiments were conducted on three query spelling correction datasets, including the well-known TREC benchmark data. Our experimental results demonstrate that the proposed method considerably outperforms existing baseline systems in terms of accuracy. Importantly, the proposed method is about one-order of magnitude faster than baseline systems in terms of training speed. In contrast to existing methods which typically require more than (e.g.,) 50 training passes, our algorithm can very closely approach the empirical optimum in around five passes.
Social web in disaster archives
Authors: Michiaki Tatsubori, Hideo Watanabe, Akihiro Shibayama, Shosuke Sato, Fumihiko Imamura
Venue: WWW
Year: 2012

Preserving social Web datasets is a crucial part of research work for disaster management based on information from social media. This paper describes the Michinoku Shinrokuden disaster archive project, mainly dedicated to archiving data from the 2011 Great East Japan Earthquake and its aftermath. Social websites should of course be part of this archive. We discuss issues in archiving social websites for the disaster management research communities and introduce our vision for Michinoku Shinrokuden.
Who killed my battery?: analyzing mobile browser energy consumption
Authors: Narendran Thiagarajan, Gaurav Aggarwal, Angela Nicoara, Dan Boneh, Jatinder Pal Singh
Venue: WWW
Year: 2012

Despite the growing popularity of mobile web browsing, the energy consumed by a phone browser while surfing the web is poorly understood. We present an infrastructure for measuring the precise energy used by a mobile browser to render web pages. We then measure the energy needed to render financial, e-commerce, email, blogging, news and social networking sites. Our tools are sufficiently precise to measure the energy needed to render individual web elements, such as cascade style sheets (CSS), Javascript, images, and plug-in objects. Our results show that for popular sites, downloading and parsing cascade style sheets and Javascript consumes a significant fraction of the total energy needed to render the page. Using the data we collected we make concrete recommendations on how to design web pages so as to minimize the energy needed to render the page. As an example, by modifying scripts on the Wikipedia mobile site we reduced by 30% the energy needed to download and render Wikipedia pages with no change to the user experience. We conclude by estimating the point at which offloading browser computations to a remote proxy can save energy on the phone.
Incorporating seasonal time series analysis with search behavior information in sales forecasting
Authors: Yuchen Tian, Yiqun Liu, Danqing Xu, Ting Yao, Min Zhang, Shaoping Ma
Venue: WWW
Year: 2012

We consider the problem of predicting monthly auto sales in mainland China. First, we design an algorithm using click-through and query reformulation information to cluster related queries and count their frequencies on monthly-basis. By introducing Exponentially Weighted Moving Averages (EWMA) model, we measure the seasonal impact on the sales trend. Two features are combined using linear regression. The experiment shows that our model is effective with high accuracy and outperforms conventional forecasting models.1
Photo-TaPE: user privacy preferences in photo tagging
Authors: Vincent Toubiana, Vincent Verdot, Benoit Christophe, Mathieu Boussard
Venue: WWW
Year: 2012

Although they are used to expose pictures on the Web, users may not want to have a link between their identity and pictures without being able to modify them or control who accesses them. Photo tagging --- and more broadly face-recognition algorithms --- often escapes to the users' control and creates links between private situations and their public profile. To address this issue, we designed a geo-location aided system to let users declare their tagging preferences directly when the picture is taken. We present Photo-Tagging Preference Enforcement (Photo-TaPE) a system enforcing users tagging preferences without revealing their identity. By improving face-recognition efficiency, Photo-TaPE can guarantee the user tagging preferences in 67% of the cases and significantly reduces the processing time of face-recognition algorithms.
Scalable multi stage clustering of tagged micro-messages
Authors: Oren Tsur, Adi Littman, Ari Rappoport
Venue: WWW
Year: 2012

The growing popularity of microblogging backed by services like Twitter, Facebook, Google+ and LinkedIn, raises the challenge of clustering short and extremely sparse documents. In this work we propose SMSC -- a scalable, accurate and efficient multi stage clustering algorithm. Our algorithm leverages users practice of adding tags to some messages by bootstrapping over virtual non sparse documents. We experiment on a large corpus of tweets from Twitter, and evaluate results against a gold-standard classification validated by seven clustering evaluation measures (information theoretic, paired and greedy). Results show that the algorithm presented is both accurate and efficient, significantly outperforming other algorithms. Under reasonable practical assumptions, our algorithm scales up sublinearly in time.
Towards minimal test collections for evaluation of audio music similarity and retrieval
Authors: Julián Urbano, Markus Schedl
Venue: WWW
Year: 2012

Reliable evaluation of Information Retrieval systems requires large amounts of relevance judgments. Making these annotations is quite complex and tedious for many Music Information Retrieval tasks, so performing such evaluations requires too much effort. A low-cost alternative is the application of Minimal Test Collection algorithms, which offer quite reliable results while significantly reducing the annotation effort. The idea is to incrementally select what documents to judge so that we can compute estimates of the effectiveness differences between systems with a certain degree of confidence. In this paper we show a first approach towards its application to the evaluation of the Audio Music Similarity and Retrieval task, run by the annual MIREX evaluation campaign. An analysis with the MIREX 2011 data shows that the judging effort can be reduced to about 35% to obtain results with 95% confidence.
Enabling users to create their own web-based machine translation engine
Authors: Andrejs Vasiljevs, Raivis Skadins, Indra Samite
Venue: WWW
Year: 2012

This paper presents European Union co-funded projects to advance the development and use of machine translation (MT) that will benefit from the possibilities provided by the Web. Current mass-market and online MT systems are of a general nature and perform poorly for smaller languages and domain specific texts. The ICT-PSP Programme project LetsMT! develops a user-driven machine translation "factory in the cloud" enabling web users to get customized MT that better fits their needs. Harnessing the huge potential of the web together with open statistical machine translation (SMT) technologies LetsMT! has created an innovative online collaborative platform for data sharing and building MT. Users can upload their parallel corpora to an online repository and generate user-tailored SMT systems based on user selected data. FP7 Programme project ACCURAT researches new methods for accumulating more data from the Web to improve the quality of data-driven machine translation systems. ACCURAT has created techniques and tools to use comparable corpora such as news feeds and multinational web pages. Although the majority of these texts are not direct translations, they share a lot of common paragraphs, sentences, phrases, terms and named entities in different languages which are useful for machine translation.
Seeing the best and worst of everything on the web with a two-level, feature-rich affect lexicon
Authors: Tony Veale
Venue: WWW
Year: 2012

Affect lexica are useful for sentiment analysis because they map words (or senses) onto sentiment ratings. However, few lexica explain their ratings, or provide sufficient feature richness to allow a selective "spin"" to be placed on a word in context. Since an affect lexicon aims to capture the affect of a word or sense in its most stereotypical usage, it should be grounded in explicit stereotype representations of each word's most salient properties and behaviors. We show here how to acquire a large stereotype lexicon from Web content, and further show how to determine sentiment ratings for each entry in the lexicon, both at the level of properties and behaviors and at the level of stereotypes. Finally, we show how the properties of a stereotype can be segregated on demand, to place a positive or negative spin on a word in context.
In the mood for affective search with web stereotypes
Authors: Tony Veale, Yanfen Hao
Venue: WWW
Year: 2012

Models of sentiment analysis in text require an understanding of what kinds of sentiment-bearing language are generally used to describe specific topics. Thus, fine-grained sentiment analysis requires both a topic lexicon and a sentiment lexicon, and an affective mapping between both. For instance, when one speaks disparagingly about a city (like London, say), what aspects of city does one generally focus on, and what words are used to disparage those aspects? As when we talk about the weather, our language obeys certain familiar patterns - what we might call clichés and stereotypes - when we talk about familiar topics. In this paper we describe the construction of an affective stereotype lexicon, that is, a lexicon of stereotypes and their most salient affective qualities. We show, via a demonstration system called MOODfinger, how this lexicon can be used to underpin the processes of affective query expansion and summarization in a system for retrieving and organizing news content from the Web. Though we adopt a simple bipolar +/- view of sentiment, we show how this stereotype lexicon allows users to coin their own nuanced moods on demand.
Conversations reconstruction in the social web
Authors: Juan Antonio Lossio-Ventura, Hakim Hacid, Arnaud Ansiaux, Maria Laura Maag
Venue: WWW
Year: 2012

We propose a socio-semantic approach for building conversations from social interactions following three steps: (i) content linkage, (ii) participants (users) linkage, and (iii) temporal linkage. Preliminary evaluations on a Twitter dataset show promising and interesting results.
Personalized newscasts and social networks: a prototype built over a flexible integration model
Authors: Luca Vignaroli, Roberto Del Pero, Fulvio Negro
Venue: WWW
Year: 2012

The way we watch television is changing with the introduction of attractive Web activities that move users away from TV to other media. The integration of the cultures of TV and Web is still an open issue. How can we make TV more open? How can we enable a possible collaboration of these two different worlds? TV-Web convergence is much more than placing a Web browser into a TV set or putting TV content into a Web media player. The NoTube project, funded by the European Community, is demonstrating how an open and general set of tools adaptable to a number of possible scenarios and allowing a designer to implement the targeted final service with ease can be introduced. A prototype based on the NoTube model in which the Smartphone is used as secondary screen is presented. The video demonstration [11] is available at http://youtu.be/dMM7MH9CZY8.
A flexible generative model for preference aggregation
Authors: Maksims Volkovs, Richard S. Zemel
Venue: WWW
Year: 2012

Many areas of study, such as information retrieval, collaborative filtering, and social choice face the preference aggregation problem, in which multiple preferences over objects must be combined into a consensus ranking. Preferences over items can be expressed in a variety of forms, which makes the aggregation problem difficult. In this work we formulate a flexible probabilistic model over pairwise comparisons that can accommodate all these forms. Inference in the model is very fast, making it applicable to problems with hundreds of thousands of preferences. Experiments on benchmark datasets demonstrate superior performance to existing methods