OpenRaiser/Intern-Atlas huggingface dataset:
CrowdReranking: exploring multiple search engines for visual search reranking
Authors: Yuan Liu, Tao Mei, Xian-Sheng Hua
Venue: SIGIR
Year: 2009

Most existing approaches to visual search reranking predominantly focus on mining information within the initial search results. However, the initial ranked list cannot provide enough cues for reranking by itself due to the typically unsatisfying visual search performance. This paper presents a new method for visual search reranking called CrowdReranking, which is characterized by mining relevant visual patterns from image search results of multiple search engines which are available on the Internet. Observing that different search engines might have different data sources for indexing and methods for ranking, it is reasonable to assume that there exist different search results yet certain common visual patterns relevant to a given query among those results. We first construct a set of visual words based on the local image patches collected from multiple image search engines. We then explicitly detect two kinds of visual patterns, i.e., salient and concurrent patterns, among the visual words. Theoretically, we formalize reranking as an optimization problem on the basis of the mined visual patterns and propose a close-form solution. Empirically, we conduct extensive experiments on several real-world search engines and one benchmark dataset, and show that the proposed CrowdReranking is superior to the state-of-the-art works.
Is this urgent?: exploring time-sensitive information needs in collaborative question answering
Authors: Yandong Liu, Nitya Narasimhan, Venu Vasudevan, Eugene Agichtein
Venue: SIGIR
Year: 2009

As online Collaborative Question Answering (CQA) servicessuch as Yahoo! Answers and Baidu Knows are attracting users, questions, and answers at an explosive rate, the truly urgent and important questions are increasingly getting lost in the crowd. That is, questions that require immediate responses are pushed out of the way by the trivial but more recently arriving questions. Unlike other questions in collaborative question answering (CQA) for which users might be willing to wait until good answers appear, urgent questions are likely to be of interest to the asker only if answered in the next few minutes or hours. For such questions, late responses are either not useful or are simply not applicable. Unfortunately, current collaborative question-answering systems do not distinguish urgent questions from the rest, and could thus be ineffective for urgent information needs. We explore text- and data- mining methods for automatically identifying urgent questions in the CQA setting. Our results indicate that modeling the question context (i.e., the particular forum/category where the question was posted) can increase classification accuracy compared to the text of the question alone.
Positional language models for information retrieval
Authors: Yuanhua Lv, ChengXiang Zhai
Venue: SIGIR
Year: 2009

Although many variants of language models have been proposed for information retrieval, there are two related retrieval heuristics remaining "external" to the language modeling approach: (1) proximity heuristic which rewards a document where the matched query terms occur close to each other; (2) passage retrieval which scores a document mainly based on the best matching passage. Existing studies have only attempted to use a standard language model as a "black box" to implement these heuristics, making it hard to optimize the combination parameters.
Page hunt: improving search engines using human computation games
Authors: Hao Ma, Raman Chandrasekar, Chris Quirk, Abhishek Gupta
Venue: SIGIR
Year: 2009

There has been a lot of work on evaluating and improving the relevance of web search engines. In this paper, we suggest using human computation games to elicit data from players that can be used to improve search. We describe Page Hunt, a single-player game. The data elicited using Page Hunt has several applications including providing metadata for pages, providing query alterations for use in query refinement, and identifying ranking issues. We describe an experiment with over 340 game players, and highlight some interesting aspects of the data obtained.
On perfect document rankings for expert search
Authors: Craig Macdonald, Iadh Ounis
Venue: SIGIR
Year: 2009

Expert search systems often employ a document search component to identify on-topic documents, which are then used to identify people likely to have relevant expertise. This work investigates the impact of the retrieval effectiveness of the underlying document search component. It has been previously shown that applying techniques to the underlying document search component that normally improve the effectiveness of a document search engine also have a positive impact on the retrieval effectiveness of the expert search engine. In this work, we experiment with fictitious perfect document rankings, to attempt to identify an upper-bound in expert search system performance. Our surprising results infer that non-relevant documents can bring useful expertise evidence, and that removing these does not lead to an upper-bound in retrieval performance.
Is spam an issue for opinionated blog post search?
Authors: Craig Macdonald, Iadh Ounis, Ian Soboroff
Venue: SIGIR
Year: 2009

In opinion-finding, the retrieval system is tasked with retrieving not just relevant documents, but those that also express an opinion towards the query target entity. This task has been studied in the context of the blogosphere by groups participating in the 2006-2008 TREC Blog tracks. Spam blogs (splogs) are thought to be a problem on the blogosphere. In this paper, we investigate the extent to which spam has affected the participating groups' retrieval systems over the three years of the TREC Blog track opinion-finding task. Our results show that spam can be an issue, with most systems retrieving some spam for every topic. However, removing spam from the rankings does not markedly change the relative performance of opinion-finding approaches.
Usefulness of click-through data in expert search
Authors: Craig Macdonald, Ryen W. White
Venue: SIGIR
Year: 2009

The task in expert finding is to identify members of an organisation with relevant expertise on a given topic. Typically, an expert search engine uses evidence from the authors of on-topic documents found in the organisation's intranet by search engines. The search result click-through behaviour of many intranet search engine users provides an additional source of evidence to identify topically-relevant documents, and via document authorship, experts. In this poster, we assess the usefulness of click-through log data for expert finding. We find that ranking authors based solely on the clicks their documents receive is reasonably effective at correctly identifying relevant experts. Moreover, we show that this evidence can successfully be integrated with an existing expert search engine to increase its retrieval effectiveness.
Web searching for daily living
Authors: Takuya Maekawa, Yutaka Yanagisawa, Yasushi Sakurai, Yasue Kishino, Koji Kamei, Takeshi Okadome
Venue: SIGIR
Year: 2009

The new concept proposed in this paper is a query free web search that automatically retrieves a web page including information related to the daily activity that we are currently engaged in for automatically displaying the page on Internet-connected domestic appliances around us such as televisions. When we are washing a coffee maker, for example, a web page is retrieved that includes tips such as `cleaning a coffee maker with vinegar removes stains well.' A method designed on the basis of this concept automatically searches for a web page by using a query constructed from the use of ordinary household objects that is detected by sensors attached to the objects. An in-situ experiment tests a variety of IR techniques and the experiment confirmed that our daily activities can produce related web pages with high accuracy.
A unified inverted index for an efficient image and text retrieval
Authors: Jonathan Mamou, Yosi Mass, Michal Shmueli-Scheuer, Benjamin Sznajder
Venue: SIGIR
Year: 2009

We present an efficient method for approximate search in a combination of several metric spaces -- which are a generalization of low level image features -- using an inverted index. Our approximation gives very high recall with subsecond response time on a real data set of one million images extracted from Flickr. We further exploit the inverted index to improve efficiency of the query processing by combining our search in metric features with search in associated textual metadata.
Cross language name matching
Authors: J. Scott McCarley
Venue: SIGIR
Year: 2009

Cross language information retrieval methods are used to determine which segments of Arabic language documents match name-based English queries. We investigate and contrast a word-based translation model with a character-based transliteration model in order to handle spelling variation and previously unseen names. We measure performance by making a novel use of the training data from the 2007 ACE Entity Translation
On single-pass indexing with MapReduce
Authors: Richard McCreadie, Craig Macdonald, Iadh Ounis
Venue: SIGIR
Year: 2009

Indexing is an important Information Retrieval (IR) operation, which must be parallelised to support large-scale document corpora. We propose a novel adaptation of the state-of-the-art single-pass indexing algorithm in terms of the MapReduce programming model. We then experiment with this adaptation, in the context of the Hadoop MapReduce implementation. In particular, we explore the scale of improvements that can be achieved when using firstly more processing hardware and secondly larger corpora. Our results show that indexing speed increases in a close to linear fashion when scaling corpus size or number of processing machines. This suggests that the proposed indexing implementation is viable to support upcoming large-scale corpora.
Addressing morphological variation in alphabetic languages
Authors: Paul McNamee, Charles K. Nicholas, James Mayfield
Venue: SIGIR
Year: 2009

The selection of indexing terms for representing documents is a key decision that limits how effective subsequent retrieval can be. Often stemming algorithms are used to normalize surface forms, and thereby address the problem of not finding documents that contain words related to query terms through infectional or derivational morphology. However, rule-based stemmers are not available for every language and it is unclear which methods for coping with morphology are most effective. In this paper we investigate an assortment of techniques for representing text and compare these approaches using data sets in eighteen languages and five different writing systems.
An evaluation of entity and frequency based query completion methods
Authors: Edgar Meij, Peter Mika, Hugo Zaragoza
Venue: SIGIR
Year: 2009

We present a semantic approach to suggesting query completions which leverages entity and type information. When compared to a frequency-based approach, we show that such information mostly helps rare queries.
Integrating clusters created offline with query-specific clusters for document retrieval
Authors: Lior Meister, Oren Kurland, Inna Gelfer Kalmanovich
Venue: SIGIR
Year: 2009

Previous work on cluster-based document retrieval has used either static document clusters that are created offline, or query-specific (dynamic) document clusters that are created from top-retrieved documents. We present the potential merit of integrating these two types of clusters.
A comparison of retrieval-based hierarchical clustering approaches to person name disambiguation
Authors: Christof Monz, Wouter Weerkamp
Venue: SIGIR
Year: 2009

This paper describes a simple clustering approach to person name disambiguation of retrieved documents. The methods are based on standard IR concepts and do not require any task-specific features. We compare different term-weighting and indexing methods and evaluate their performance against the Web People Search task (WePS). Despite their simplicity these approaches achieve very competitive performance.
An automatic translation of tags for multimedia contents using folksonomy networks
Authors: Tae-Gil Noh, Seong-Bae Park, Hee-Geun Yoon, Sang-Jo Lee, Se-Young Park
Venue: SIGIR
Year: 2009

This paper proposes a novel method to translate tags attached to multimedia contents for cross-language retrieval. The main issue in this problem is the sense disambiguation of tags given with few textual contexts. In order to solve this problem, the proposed method represents both tags and its translation candidates as networks of co-occurring tags since a network allows richer expression of contexts than other expressions such as co-occurrence vectors. The method translates a tag by selecting the optimal one from possible candidates based on a network similarity even when neither the textual contexts nor sophisticated language resources are available. The experiments on the MIR Flickr-2008 test set show that the proposed method achieves 90.44% accuracy in translating tags from English into German, which is significantly higher than the baseline methods of a frequency based translation and a co-occurrence-based translation.
Classifying library catalogue by author profiling
Authors: Tadashi Nomoto
Venue: SIGIR
Year: 2009

This paper presents a novel approach to classifying library records by making use of what we call "author profile," a representation of an author's expertise along a library classification. Coupled with a string kernel classifier, the idea is shown to bring a significant improvement over a baseline.
Generic similarity search engine demonstrated by an image retrieval application
Authors: David Novak, Michal Batko, Pavel Zezula
Venue: SIGIR
Year: 2009

We introduce a generic engine for large-scale similarity search and demonstrate it on a set of 100 million Flickr images.
Combining LVCSR and vocabulary-independent ranked utterance retrieval for robust speech search
Authors: J. Scott Olsson, Douglas W. Oard
Venue: SIGIR
Year: 2009

Well tuned Large-Vocabulary Continuous Speech Recognition (LVCSR) has been shown to generally be more effective than vocabulary-independent techniques for ranked retrieval of spoken content when one or the other approach is used alone. Tuning LVCSR systems to a topic domain can be costly, however, and the experiments in this paper show that Out-Of-Vocabulary (OOV) query terms can significantly reduce retrieval effectiveness when that tuning is not performed. Further experiments demonstrate, however, that retrieval effectiveness for queries with OOV terms can be substantially improved by combining evidence from LVCSR with additional evidence from vocabulary-independent Ranked Utterance Retrieval (RUR). The combination is performed by using relevance judgments from held-out topics to learn generic (i.e., topic-independent), smooth, non-decreasing transformations from LVCSR and RUR system scores to probabilities of topical relevance. Evaluated using a CLEF collection that includes topics, spontaneous conversational speech audio, and relevance judgments, the system recovers 57% of the mean uninterpolated average precision that could have been obtained through LVCSR domain tuning for very short queries (or 41% for longer queries).
Compression-based document length prior for language models
Authors: Javier Parapar, David E. Losada, Alvaro Barreiro
Venue: SIGIR
Year: 2009

The inclusion of document length factors has been a major topic in the development of retrieval models. We believe that current models can be further improved by more refined estimations of the document's scope. In this poster we present a new document length prior that uses the size of the compressed document. This new prior is introduced in the context of Language Modeling with Dirichlet smoothing. The evaluation performed on several collections shows significant improvements in effectiveness.
Spoken information retrieval for turkish broadcast news
Authors: Siddika Parlak, Murat Saraclar
Venue: SIGIR
Year: 2009

Speech Retrieval systems utilize automatic speech recognition (ASR) to generate textual data for indexing. However, automatic transcriptions include errors, either because of out-of-vocabulary (OOV) words or due to ASR inaccuracy. In this work, we address spoken information retrieval in Turkish, a morphologically rich language where OOV rates are high. We apply several techniques, such as using subword units and indexing alternative hypotheses, to cope with the OOV problem and ASR inaccuracy.
Web-derived resources for web information retrieval: from conceptual hierarchies to attribute hierarchies
Authors: Marius Pasca, Enrique Alfonseca
Venue: SIGIR
Year: 2009

A weakly-supervised extraction method identifies concepts within conceptual hierarchies, at the appropriate level of specificity (e.g., Bank vs. Institution), to which attributes (e.g., routing number) extracted from unstructured text best apply. The extraction exploits labeled classes of instances acquired from a combination of Web documents and query logs, and inserted into existing conceptual hierarchies. The correct concept is identified within the top three positions on average over gold-standard attributes, which corresponds to higher accuracy than in alternative experiments.
Equivalence between nonnegative tensor factorization and tensorial probabilistic latent semantic analysis
Authors: Wei Peng
Venue: SIGIR
Year: 2009

This paper establishes a connection between NMF and PLSA on multi-way data, called NTF and T-PLSA respectively. Two types of T-PLSA models are proven to be equivalent to non-negative PARAFAC and non-negative Tucker3. This paper also shows that by running NTF and T-PLSA alternatively, they can jump out of each other's local minima and achieve a better clustering solution.
Score and rank convergence of HITS
Authors: Enoch Peserico, Luca Pretto
Venue: SIGIR
Year: 2009

How many iterations does the (ever more) popular HITS algorithm require to converge in score and, perhaps more importantly, in rank (i.e. to get the nodes of a graph "in the right order")? After pinning down the elusive notion of convergence in rank we provide the first non-trivial bounds on the convergence of HITS. A "worst case" example, requiring a number of iterations superexponential in the size of the target graph to achieve even "mild" convergence, suggests the need for greater caution in the experimental evaluation of the algorithm - as recent results of poor performance (e.g. vs. SALSA) might be due to insufficient iterations, rather than to an intrinsic deficiency of HITS. An almost matching upper bound shows that, as long as one employs exponential acceleration e.g. through a "squaring trick", a polynomial running time (practical in many application domains) always provides strong convergence guarantees.
Measuring the descriptiveness of web comments
Authors: Martin Potthast
Venue: SIGIR
Year: 2009

This paper investigates whether Web comments are of descriptive nature, that is, whether the combined text of a set of comments is similar in topic to the commented object. If so, comments may be used in place of the respective object in all kinds of cross-media retrieval tasks. Our experiments reveal that comments on textual objects are indeed descriptive: 10 comments suffice to expect a high similarity between the comments and the commented text; 100-500 comments suffice to replace the commented text in a ranking task, and to measure the contribution of the commenters beyond the commented text.
Topic prerogative feature selection using multiple query examples for automatic video retrieval
Authors: P. Punitha, Joemon M. Jose, Anuj Goyal
Venue: SIGIR
Year: 2009

Well acceptance of relevance feedback and collaborative systems has given the users to express their preferences in terms of multiple query examples. The technology devised to utilize these user preferences, is expected to mine the semantic knowledge embedded within these query examples. In this paper, we propose a video mining framework based on dynamic learning from queries, using a statistical model for topic prerogative feature selection. The proposed method is specifically designed for multiple query example scenarios. The effectiveness of the proposed framework has been established with an extensive experimentation on TRECVid2007 data collection. The results reveal that our approach achieves a performance that is in par with the best results for this corpus without the requirement of any textual data.
A relevance model based filter for improving ad quality
Authors: Hema Raghavan, Dustin Hillard
Venue: SIGIR
Year: 2009

Recently there has been a surge in research that predicts retrieval relevance using historical click-through data. While a larger number of clicks between a query and a document provides a stronger ``confidence" of relevance, most models in the literature that learn from clicks are error-prone as they do not take into account any confidence estimates. Sponsored Search models are especially prone to this error as they are typically trained on search engine logs in order to predict click-through-rate (CTR). The estimated CTR ultimately determines the rank at which an ad is shown and also impacts the price (cost-per-click) for the advertiser. In this paper, we improve a model that applies collaborative filtering on click data by training a filter that has been trained to predict pure relevance. Applying the filter to ads that have seen few clicks on live traffic results in improved CTR and click-yield (CY). Additionally, in offline experiments we find that using features based on the \emph{organic} results improves the relevance based filter's performance.
System scoring using partial prior information
Authors: Sri Devi Ravana, Laurence Anthony F. Park, Alistair Moffat
Venue: SIGIR
Year: 2009

We introduce smoothing of retrieval effectiveness scores, which balances results from prior incomplete query sets against limited additional complete information, in order to obtain more refined system orderings than would be possible on the new queries alone.
Incentives for social annotation
Authors: Heather Roinestad, John Burgoon, Benjamin Markines, Filippo Menczer
Venue: SIGIR
Year: 2009

The effectiveness of community-driven annotation, such as social bookmarking, depends on user participation. Since the participation of many users is motivated by selfish reasons, an effective way to encourage participation is to create useful or entertaining applications. We demo two such tools -- a browser extension and a game.