OpenRaiser/Intern-Atlas huggingface dataset:
$100, 000 prize jackpot. call now!: identifying the pertinent features of SMS spam
Authors: Henry Tan, Nazli Goharian, Micah Sherr
Venue: SIGIR
Year: 2012

Mobile SMS spam is on the rise and is a prevalent problem. While recent work has shown that simple machine learning techniques can distinguish between ham and spam with high accuracy, this paper explores the individual contributions of various textual features in the classification process. Our results reveal the surprising finding that simple is better: using the largest spam corpus of which we are aware, we find that using simple textual features is sufficient to provide accuracy that is nearly identical to that achieved by the best known techniques, while achieving a twofold speedup.
Exploiting temporal topic models in social media retrieval
Authors: Tuan Tran
Venue: SIGIR
Year: 2012

Many of user generated contents in the Web 2.0 center around real-world incidents such as Japanese tsunami, or general concerns such as recent economic downturn. Such type of information is always of interest to users. For instance, when a user reads a news article about a tsunami in Japan, she wants to see related Flickr photos or more tweets about it. Conventional keyword-based search is inappropriate, since it is not always trivial to formulate ad-hoc interests about the event and material. In some cases, the user might want to explore emerging topics that dominate different sources. Present systems fail to connect topically documents across media, and the user has to examine individual sources to infer the topics herself.
Using eye-tracking with dynamic areas of interest for analyzing interactive information retrieval
Authors: Vu Tuan Tran, Norbert Fuhr
Venue: SIGIR
Year: 2012

Based on a new framework for capturing dynamic areas of interest in eye-tracking, we model the user search process as a Markov-chain. The analysis indicates possible system improvements and yields parameter estimates for the Interactive Probability Ranking Principle (IPRP).
Image ranking based on user browsing behavior
Authors: Michele Trevisiol, Luca Chiarandini, Luca Maria Aiello, Alejandro Jaimes
Venue: SIGIR
Year: 2012

Ranking of images is difficult because many factors determine their importance (e.g., popularity, quality, entertainment value, context, etc.). In social media platforms, ranking also depends on social interactions and on the visibility of the images both inside and outside those platforms. In this context, the application of standard ranking methods is not clearly understood, and neither are the subtleties associated with taking into account social interaction, internal, and external factors. In this paper, we use a large Flickr dataset and investigate these factors by performing an in-depth analysis of several ranking algorithms using both internal (i.e., within Flickr) and external (i.e., links from outside of Flickr) factors. We analyze rankings given by common metrics used in image retrieval (e.g., number of favorites), and compare them with metrics based on page views (e.g., time spent, number of views). In addition, we represent users' navigation by a graph and combine session models with some of these metrics, comparing with PageRank and BrowseRank. Our experiments show significant differences between the rankings, providing insights on the impact of social interactions, internal, and external factors in image ranking.
Language intent models for inferring user browsing behavior
Authors: Manos Tsagkias, Roi Blanco
Venue: SIGIR
Year: 2012

Modeling user browsing behavior is an active research area with tangible real-world applications, e.g., organizations can adapt their online presence to their visitors browsing behavior with positive effects in user engagement, and revenue. We concentrate on online news agents, and present a semi-supervised method for predicting news articles that a user will visit after reading an initial article. Our method tackles the problem using language intent models trained on historical data which can cope with unseen articles. We evaluate our method on a large set of articles and in several experimental settings. Our results demonstrate the utility of language intent models for predicting user browsing behavior within online news sites.
Looking inside the box: context-sensitive translation for cross-language information retrieval
Authors: Ferhan Türe, Jimmy Lin, Douglas W. Oard
Venue: SIGIR
Year: 2012

Cross-language information retrieval (CLIR) today is dominated by techniques that use token-to-token mappings from bilingual dictionaries. Yet, state-of-the-art statistical translation models (e.g., using Synchronous Context-Free Grammars) are far richer, capturing multi-term phrases, term dependencies, and contextual constraints on translation choice. We present a novel CLIR framework that is able to reach inside the translation "black box" and exploit these sources of evidence. Experiments on the TREC-5/6 English-Chinese test collection show this approach to be promising.
Personalized diversification of search results
Authors: David Vallet, Pablo Castells
Venue: SIGIR
Year: 2012

Search personalization and diversification are often seen as opposing alternatives to cope with query uncertainty, where, given an ambiguous query, it is either preferable to adapt the search result to a specific aspect that may interest the user (personalization) or to regard multiple aspects in order to maximize the probability that some query aspect is relevant to the user (diversification). In this work, we question this antagonistic view, and hypothesize that these two directions may in fact be effectively combined and enhance each other. We research the introduction of the user as an explicit random variable in state of the art diversification methods, thus developing a generalized framework for personalized diversification. In order to evaluate our hypothesis, we conduct an evaluation with real users using crowdsourcing services. The obtained results suggest that the combination of personalization and diversification achieves competitive performance, improving the base-line, plain personalization, and plain diversification approaches in terms of both diversity and accuracy measures.
Explicit relevance models in intent-oriented information retrieval diversification
Authors: Saul Vargas, Pablo Castells, David Vallet
Venue: SIGIR
Year: 2012

The intent-oriented search diversification methods developed in the field so far tend to build on generative views of the retrieval system to be diversified. Core algorithm components in particular redundancy assessment are expressed in terms of the probability to observe documents, rather than the probability that the documents be relevant. This has been sometimes described as a view considering the selection of a single document in the underlying task model. In this paper we propose an alternative formulation of aspect-based diversification algorithms which explicitly includes a formal relevance model. We develop means for the effective computation of the new formulation, and we test the resulting algorithm empirically. We report experiments on search and recommendation tasks showing competitive or better performance than the original diversification algorithms. The relevance-based formulation has further interesting properties, such as unifying two well-known state of the art algorithms into a single version. The relevance-based approach opens alternative possibilities for further formal connections and developments as natural extensions of the framework. We illustrate this by modeling tolerance to redundancy as an explicit configurable parameter, which can be set to better suit the characteristics of the IR task, or the evaluation metrics, as we illustrate empirically.
Group matrix factorization for scalable topic modeling
Authors: Quan Wang, Zheng Cao, Jun Xu, Hang Li
Venue: SIGIR
Year: 2012

Topic modeling can reveal the latent structure of text data and is useful for knowledge discovery, search relevance ranking, document classification, and so on. One of the major challenges in topic modeling is to deal with large datasets and large numbers of topics in real-world applications. In this paper, we investigate techniques for scaling up the non-probabilistic topic modeling approaches such as RLSI and NMF. We propose a general topic modeling method, referred to as Group Matrix Factorization (GMF), to enhance the scalability and efficiency of the non-probabilistic approaches. GMF assumes that the text documents have already been categorized into multiple semantic classes, and there exist class-specific topics for each of the classes as well as shared topics across all classes. Topic modeling is then formalized as a problem of minimizing a general objective function with regularizations and/or constraints on the class-specific topics and shared topics. In this way, the learning of class-specific topics can be conducted in parallel, and thus the scalability and efficiency can be greatly improved. We apply GMF to RLSI and NMF, obtaining Group RLSI (GRLSI) and Group NMF (GNMF) respectively. Experiments on a Wikipedia dataset and a real-world web dataset, each containing about 3 million documents, show that GRLSI and GNMF can greatly improve RLSI and NMF in terms of scalability and efficiency. The topics discovered by GRLSI and GNMF are coherent and have good readability. Further experiments on a search relevance dataset, containing 30,000 labeled queries, show that the use of topics learned by GRLSI and GNMF can significantly improve search relevance.
Summarizing the differences from microblogs
Authors: Dingding Wang, Mitsunori Ogihara, Tao Li
Venue: SIGIR
Year: 2012

With the rapid growth of social media websites, microblogging has become a popular way to spread instant news and events. Due to the dynamic and social nature of microblogs, extracting useful information from microblogs is more challenging than from the traditional news articles. In this paper we study the problem of summarizing the differences from microblogs. Given a collection of microblogs discussing an event/topic, we propose to generate a short summary delivering the differences among these microblogs, such as the different points of view for a news topic and the changes and evolution of an ongoing event.
Effect of written instructions on assessor agreement
Authors: William Webber, Bryan Toth, Marjorie Desamito
Venue: SIGIR
Year: 2012

Assessors frequently disagree on the topical relevance of documents. How much of this disagreement is due to ambiguity in assessment instructions? We have two assessors assess TREC Legal Track documents for relevance, some to a general topic description, others to detailed assessment guidelines. We find that detailed guidelines lead to no significant increase in agreement amongst assessors or between assessors and the official qrels.
Political search trends
Authors: Ingmar Weber, Venkata Rama Kiran Garimella, Erik Borra
Venue: SIGIR
Year: 2012

We present Political Search Trends, a browser based web search analysis tool that (i) assigns a political leaning to web search queries, (ii) detects trending political queries in a given week, and (iii) links search queries to fact-checked statements. In terms of methodology, it showcases the power of analyzing queries leading to clicks on selected, annotated web sites of interest.
Fighting against web spam: a novel propagation method based on click-through data
Authors: Chao Wei, Yiqun Liu, Min Zhang, Shaoping Ma, Liyun Ru, Kuo Zhang
Venue: SIGIR
Year: 2012

Combating Web spam is one of the greatest challenges for Web search engines. State-of-the-art anti-spam techniques focus mainly on detecting varieties of spam strategies, such as content spamming and link-based spamming. Although these anti-spam approaches have had much success, they encounter problems when fighting against a continuous barrage of new types of spamming techniques. We attempt to solve the problem from a new perspective, by noticing that queries that are more likely to lead to spam pages/sites have the following characteristics: 1) they are popular or reflect heavy demands for search engine users and 2) there are usually few key resources or authoritative results for them. From these observations, we propose a novel method that is based on click-through data analysis by propagating the spamicity score iteratively between queries and URLs from a few seed pages/sites. Once we obtain the seed pages/sites, we use the link structure of the click-through bipartite graph to discover other pages/sites that are likely to be spam. Experiments show that our algorithm is both efficient and effective in detecting Web spam. Moreover, combining our method with some popular anti-spam techniques such as TrustRank achieves improvement compared with each technique taken individually.
Studies of the onset and persistence of medical concerns in search logs
Authors: Ryen W. White, Eric Horvitz
Venue: SIGIR
Year: 2012

The Web provides a wealth of information about medical symptoms and disorders. Although this content is often valuable to consumers, studies have found that interaction with Web content may heighten anxiety and stimulate healthcare utilization. We present a longitudinal log-based study of medical search and browsing behavior on the Web. We characterize how users focus on particular medical concerns and how concerns persist and influence future behavior, including changes in focus of attention in searching and browsing for health information. We build and evaluate models that predict transitions from searches on symptoms to searches on health conditions, and escalations from symptoms to serious illnesses. We study the influence that the prior onset of concerns may have on future behavior, including sudden shifts back to searching on the concern amidst other searches. Our findings have implications for refining Web search and retrieval to support people pursuing diagnostic information.
Effects of expertise differences in synchronous social Q&A
Authors: Ryen W. White, Matthew Richardson
Venue: SIGIR
Year: 2012

Synchronous social question-and-answer (Q&A) systems match askers to answerers and support real-time dialog between them to resolve questions. These systems typically find answerers based on the degree of expertise match with the asker's initial question. However, since synchronous social Q&A involves a dialog between asker and answerer, differences in expertise may also matter (e.g., extreme novices and experts may have difficulty establishing common ground). In this poster we use data from a live social Q&A system to explore the impact of expertise differences on answer quality and aspects of the dialog itself. The findings of our study suggest that synchronous social Q&A systems should consider the relative expertise of candidate answerers with respect to the asker, and offer interactive dialog support to help establish common ground between askers and answerers.
The essence of time: considering temporal relevance as an intent-aware ranking problem
Authors: Stewart Whiting
Venue: SIGIR
Year: 2012

Real-time news and social media quickly reflect large-scale phenomena and events. As users become exposed to this information, time plays a central role in prompting both information authorship and seeking activities.
On automatically tagging web documents from examples
Authors: Nicholas Joel Woodward, Weijia Xu, Kent Norsworthy
Venue: SIGIR
Year: 2012

An emerging need in information retrieval is to identify a set of documents conforming to an abstract description. This task presents two major challenges to existing methods of document retrieval and classification. First, similarity based on overall content is less effective because there may be great variance in both content and subject of documents produced for similar functions, e.g. a presidential speech or a government ministry white paper. Second, the function of the document can be defined based on user interests or the specific data set through a set of existing examples, which cannot be described with standard categories. Additionally, the increasing volume and complexity of document collections demands new scalable computational solutions. We conducted a case study using web-archived data from the Latin American Government Documents Archive (LAGDA) to illustrate these problems and challenges. We propose a new hybrid approach based on Naïve Bayes inference that uses mixed n-gram models obtained from a training set to classify documents in the corpus. The approach has been developed to exploit parallel processing for large scale data set. The preliminary work shows promising results with improved accuracy for this type of retrieval problem.
User evaluation of query quality
Authors: Wan-Ching Wu, Diane Kelly, Kun Huang
Venue: SIGIR
Year: 2012

Although a great deal of research has been conducted about automatic techniques for determining query quality, there have been relatively few studies about how people judge query quality. This study investigated this topic through a laboratory experiment with 40 subjects. Subjects were shown eight information problems (five fact-finding and three exploratory) and asked to evaluate queries for these problems according to several quality attributes. Subjects then evaluated search engine results pages (SERPs) for each query, which were manipulated to exhibit different levels of performance. Following this, subjects reevaluated the queries, were interviewed about their evaluation approaches and repeated the rating procedure for two information problems. Results showed that for fact-finding information problems, longer queries received higher ratings (both initial and post-SERP), and that post-SERP query ratings were more affected by the proportion of relevant documents viewed to all documents viewed rather than the ranks of the relevant documents. For exploratory information problems, subjects' ratings were highly correlated with the number of relevant documents in the SERP as well as the proportion of relevant documents viewed. Subjects adopted several approaches when evaluating query quality, which led to different quality ratings. Finally, during the reliability check subjects' initial evaluations were fairly stable, but their post-SERP evaluations significantly increased.
Boosting multi-kernel locality-sensitive hashing for scalable image retrieval
Authors: Hao Xia, Pengcheng Wu, Steven C. H. Hoi, Rong Jin
Venue: SIGIR
Year: 2012

Similarity search is a key challenge for multimedia retrieval applications where data are usually represented in high-dimensional space. Among various algorithms proposed for similarity search in high-dimensional space, Locality-Sensitive Hashing (LSH) is the most popular one, which recently has been extended to Kernelized Locality-Sensitive Hashing (KLSH) by exploiting kernel similarity for better retrieval efficacy. Typically, KLSH works only with a single kernel, which is often limited in real-world multimedia applications, where data may originate from multiple resources or can be represented in several different forms. For example, in content-based multimedia retrieval, a variety of features can be extracted to represent contents of an image. To overcome the limitation of regular KLSH, we propose a novel Boosting Multi-Kernel Locality-Sensitive Hashing (BMKLSH) scheme that significantly boosts the retrieval performance of KLSH by making use of multiple kernels. We conduct extensive experiments for large-scale content-based image retrieval, in which encouraging results show that the proposed method outperforms the state-of-the-art techniques.
Exploring tag relevance for image tag re-ranking
Authors: Jie Xiao, Wengang Zhou, Qi Tian
Venue: SIGIR
Year: 2012

In this paper, we propose to explore the relevance between tags for image tag re-ranking. The key component is to define a global tag-tag similarity matrix, which is achieved by analysis in both semantic and visual aspects. The text semantic relevance is explored by the Latent Semantic Indexing (LSI) model [1].For the visual information, the tag-relevance can be propagated by reconstructing exemplar images with visually and semantically consistent images. Based on our tag relevance matrix, a random-walk approach is leveraged to discover the significance of each tag. Finally, all tags in an image are re-ranked by their significance values. Extensive experiments show its effectiveness on an image dataset with a large tags vocabulary.
Exploring example-based person search in email
Authors: Tan Xu, Douglas W. Oard
Venue: SIGIR
Year: 2012

This paper describes an entity ranking model for example-based person search in email. Evaluation by comparison to manually resolved named references in Enron email yield results that correspond to typically placing the correct entity in the first or second rank.
Generating reformulation trees for complex queries
Authors: Xiaobing Xue, W. Bruce Croft
Venue: SIGIR
Year: 2012

Search queries have evolved beyond keyword queries. Many complex queries such as verbose queries, natural language question queries and document-based queries are widely used in a variety of applications. Processing these complex queries usually requires a series of query operations, which results in multiple sequences of reformulated queries. However, previous query representations, either the "bag of words" method or the recently proposed "query distribution" method, cannot effectively model these query sequences, since they ignore the relationships between two queries. In this paper, a reformulation tree framework is proposed to organize multiple sequences of reformulated queries as a tree structure, where each path of the tree corresponds to a sequence of reformulated queries. Specifically, a two-level reformulation tree is implemented for verbose queries. This tree effectively combines two query operations, i.e., subset selection and query substitution, within the same framework. Furthermore, a weight estimation approach is proposed to assign weights to each node of the reformulation tree by considering the relationships with other nodes and directly optimizing retrieval performance. Experiments on TREC collections show that this reformulation tree based representation significantly outperforms the state-of-the-art techniques.
Finding translations in scanned book collections
Authors: Ismet Zeki Yalniz, R. Manmatha
Venue: SIGIR
Year: 2012

This paper describes an approach for identifying translations of books in large scanned book collections with OCR errors. The method is based on the idea that although individual sentences do not necessarily preserve the word order when translated, a book must preserve the linear progression of ideas for it to be a valid translation. Consider two books in two different languages, say English and German. The English book in the collection is represented by the sequence of words (in the order they appear in the text) which appear only once in the book. Similarly, the book in German is represented by its sequence of words which appear only once. An English-German dictionary is used to transform the word sequence of the English book into German by translating individual words in place. It is not necessary to translate all the words and this method works even with small dictionaries. Both sequences are now in German and can, therefore, be aligned using a Longest Common Subsequence (LCS) algorithm. We describe two scoring functions TRANS-cs and TRANS-its which account for both the LCS length and the lengths of the original word sequences. Experiments demonstrate that TRANS-its is particularly successful in finding translations of books and outperforms several baselines including metadata search based on matching titles and authors. Experiments performed on a Europarl parallel corpus for four language pairs, English-Finnish, English-French, English-German, English-Spanish, and a scanned book collection of 50K English-German books show that the proposed method retrieves translations of books with an average MAP score of 1.0 and a speed of 10K book pair comparisons per second on a single core.