OpenRaiser/Intern-Atlas huggingface dataset:
An unsupervised Hindi stemmer with heuristic improvements
Authors: Amaresh Kumar Pandey, Tanveer J. Siddiqui
Venue: SIGIR
Year: 2008

Stemmers are used to convert inflected words into their root or stem. Stem does not necessarily correspond to linguistic root of a word. Stemming improve performance by reducing morphologically variants into same words. This paper presents an approach is to develop unsupervised Hindi stemmer. This paper focus on the development of an unsupervised stemmer for Hindi and evaluation of approach using manually segmented words. We evaluate our approach on 1000-1000 words randomly extracted words (only) from Hindi WordNet1 data base. The training data has been constructed by extracting 106403 words extracted from EMILLE2 corpus. The observed accuracy was found to be 89.9% after applying some heuristic measures. The F-score was 94.96%. As the algorithm does not require any language specific information, it can be applied to other Indian languages as well. We also evaluate the effect of stemmer in terms of reducing size of index for Hindi information retrieval task. The results have been compared with light weight stemmer [10] and UMass stemmer [17]. Test run shows that our stemmer outperforms both the stemmer.
Author-topic evolution analysis using three-way non-negative Paratucker
Authors: Wei Peng, Tao Li
Venue: SIGIR
Year: 2008

Analyzing three-way data has attracted a lot of attention recently due to the intrinsic rich structures in real-world datasets. The PARATUCKER model has been proposed to combine the axis capabilities of the Parafac model and the structural generality of the Tucker model. However, no algorithms have been developed for fitting the PARATUCKER model. In this paper, we propose TANPT algorithm to solve the PARATUCKER model. We apply the algorithm for temporal relation co-clustering on author-topic evolution. Experiments on DBLP datasets demonstrate its effectiveness.
Automatic document prior feature selection for web retrieval
Authors: Jie Peng, Craig Macdonald, Iadh Ounis
Venue: SIGIR
Year: 2008

Document prior features, such as Pagerank and URL depth, can improve the retrieval effectiveness of Web Information Retrieval (IR) systems. However, not all queries equally benefit from the application of a document prior feature. This paper aims to investigate whether the retrieval performance can be further enhanced by selecting the best document prior feature on a per-query basis. We present a novel method for selecting the best document prior feature on a per-query basis. We evaluate our technique on the TREC .GOV Web test collection and its associated TREC 2003 Web search tasks. Our experiments demonstrate the effectiveness and robustness of our proposed selection method.
Classifiers without borders: incorporating fielded text from neighboring web pages
Authors: Xiaoguang Qi, Brian D. Davison
Venue: SIGIR
Year: 2008

Accurate web page classification often depends crucially on information gained from neighboring pages in the local web graph. Prior work has exploited the class labels of nearby pages to improve performance. In contrast, in this work we utilize a weighted combination of the contents of neighbors to generate a better virtual document for classification. In addition, we break pages into fields, finding that a weighted combination of text from the target and fields of neighboring pages is able to reduce classification error by more than a third. We demonstrate performance on a large dataset of pages from the Open Directory Project and validate the approach using pages from a crawl from the Stanford WebBase. Interestingly, we find no value in anchor text and unexpected value in page titles (and especially titles of parent pages) in the virtual document.
Adaptive label-driven scaling for latent semantic indexing
Authors: Xiaojun Quan, Enhong Chen, Qiming Luo, Hui Xiong
Venue: SIGIR
Year: 2008

This paper targets on enhancing Latent Semantic Indexing (LSI) by exploiting category labels. Specifically, in the term-document matrix, the vector for each term either appearing in labels or semantically close to labels is scaled before performing Singular Value Decomposition (SVD) to boost its impact on the generated left singular vectors. As a result, the similarities among documents in the same category are increased. Furthermore, an adaptive scaling strategy is designed to better utilize the hierarchical structure of categories. Experimental results show that the proposed approach is able to significantly improve the performance of hierarchical text categorization.
Predicting when browsing context is relevant to search
Authors: Mandar Rahurkar, Silviu Cucerzan
Venue: SIGIR
Year: 2008

We investigate a representative case of sudden information need change of Web users. By analyzing search engine query logs, we show that the majority of queries submitted by users after browsing documents in the news domain are related to the most recently browsed document. We investigate ways of identifying whether a query is a good candidate for contextualization conditioned on the most recently browsed document by a user. We build a successful classifier for this task, which achieves 96% precision at 90% recall.
Exploring evaluation metrics: GMAP versus MAP
Authors: Sri Devi Ravana, Alistair Moffat
Venue: SIGIR
Year: 2008

In retrieval experiments, an effectiveness metrics is used to generate a score for each system-topic pair being tested. It is then usual to average the system-topic scores to obtain a system score, which is used for the purpose of system comparison. In this paper we explore the ramifications of using the geometric mean (GMAP), rather than the arithmetic mean (MAP) when computing an aggregate system score from a set of system-topic scores. We find that GMAP does indeed handle variability in topic difficulty more consistently than does the usual MAP aggregation method.
Successfully detecting and correcting false friends using channel profiles
Authors: Ulrich Reffle, Annette Gotscharek, Christoph Ringlstetter, Klaus U. Schulz
Venue: SIGIR
Year: 2008

The detection and correction of false friends - also called real-word errors - is a notoriously difficult problem. On realistic data, the break-even point for automatic correction so far could not be reached: the number of additional infelicitous corrections outnumbered the useful corrections. We present a new approach where we first compute a profile of the error channel for the given text. During the correction process, the profile helps to restrict attention to a small set of suspicious lexical tokens of the input text where it is plausible to assume that the token represents a false friend. Using a conventional word trigram statistics for disambiguation we obtain a correction method that can be successfully applied to unrestricted text. In experiments for OCR documents, we show significant accuracy gains by fully automatic correction of false friends.
Combining document- and paragraph-based entity ranking
Authors: Henning Rode, Pavel Serdyukov, Djoerd Hiemstra
Venue: SIGIR
Year: 2008

We study entity ranking on the INEX entity track and propose a simple graph-based ranking approach that enables to combine scores on document and paragraph level. The combined approach improves the retrieval results not only on the INEX testset, but similarly on TREC's expert finding task.
TF-IDF uncovered: a study of theories and probabilities
Authors: Thomas Roelleke, Jun Wang
Venue: SIGIR
Year: 2008

Interpretations of TF-IDF are based on binary independence retrieval, Poisson, information theory, and language modelling. This paper contributes a review of existing interpretations, and then, TF-IDF is systematically related to the probabilities P(q|d) and P(d|q). Two approaches are explored: a space of independent, and a space of disjoint terms. For independent terms, an "extreme" query/non-query term assumption uncovers TF-IDF, and an analogy of P(d|q) and the probabilistic odds O(r|d, q) mirrors relevance feedback. For disjoint terms, a relationship between probability theory and TF-IDF is established through the integral + 1/x dx = log x. This study uncovers components such as divergence from randomness and pivoted document length to be inherent parts of a document-query independence (DQI) measure, and interestingly, an integral of the DQI over the term occurrence probability leads to TF-IDF.
Efficient top-k querying over social-tagging networks
Authors: Ralf Schenkel, Tom Crecelius, Mouna Kacimi, Sebastian Michel, Thomas Neumann, Josiane Xavier Parreira, Gerhard Weikum
Venue: SIGIR
Year: 2008

Online communities have become popular for publishing and searching content, as well as for finding and connecting to other users. User-generated content includes, for example, personal blogs, bookmarks, and digital photos. These items can be annotated and rated by different users, and these social tags and derived user-specific scores can be leveraged for searching relevant content and discovering subjectively interesting items. Moreover, the relationships among users can also be taken into consideration for ranking search results, the intuition being that you trust the recommendations of your close friends more than those of your casual acquaintances.
Relevance thresholds in system evaluations
Authors: Falk Scholer, Andrew Turpin
Venue: SIGIR
Year: 2008

We introduce and explore the concept of an individual's relevance threshold as a way of reconciling differences in outcomes between batch and user experiments.
Generating diverse katakana variants based on phonemic mapping
Authors: Kazuhiro Seki, Hiroyuki Hattori, Kuniaki Uehara
Venue: SIGIR
Year: 2008

In Japanese, it is quite common for the same word to be written in several different ways. This is especially true for katakana words which are typically used for transliterating foreign languages. This ambiguity becomes critical for automatic processing such as information retrieval (IR). To tackle this problem, we propose a simple but effective approach to generating katakana variants by considering phonemic representation of the original language for a given word. The proposed approach is evaluated through an assessment of the variants it generates. Also, the impact of the generated variants on IR is studied in comparison to an existing approach using katakana rewriting rules.
Local text reuse detection
Authors: Jangwon Seo, W. Bruce Croft
Venue: SIGIR
Year: 2008

Text reuse occurs in many different types of documents and for many different reasons. One form of reuse, duplicate or near-duplicate documents, has been a focus of researchers because of its importance in Web search. Local text reuse occurs when sentences, facts or passages, rather than whole documents, are reused and modified. Detecting this type of reuse can be the basis of new tools for text analysis. In this paper, we introduce a new approach to detecting local text reuse and compare it to other approaches. This comparison involves a study of the amount and type of reuse that occurs in real documents, including TREC newswire and blog collections.
Exploiting sequential dependencies for expert finding
Authors: Pavel Serdyukov, Henning Rode, Djoerd Hiemstra
Venue: SIGIR
Year: 2008

We propose an expert finding method based on assumption of sequential dependence between a candidate expert and the query terms in the scope of a document. We assume that the strength of relation of a candidate to the document's content depends on its position in this document with respect to the positions of the query terms. The experiments on the official Enterprise TREC data demonstrate the advantage of our method over the method based on independence of query terms and persons in a document.
Modeling expert finding as an absorbing random walk
Authors: Pavel Serdyukov, Henning Rode, Djoerd Hiemstra
Venue: SIGIR
Year: 2008

We introduce a novel approach to expert finding based on multi-step relevance propagation from documents to related candidates. Relevance propagation is modeled with an absorbing random walk. The evaluation on the two official Enterprise TREC data sets demonstrates the advantage of our method over the state-of-the-art method based on onestep propagation. Categories and Subject Descriptors: H.3 [Information Storage and Retrieval]: H.3.3 Information
Understanding system implementation and user behavior in a collaborative information seeking environment
Authors: Chirag Shah
Venue: SIGIR
Year: 2008

It is well said that two minds are better than one. When it comes to accessing or processing some information, it seems natural to expect that when people work together in collaboration they (1) can accomplish more, (2) benefit from other people’s experience and expertise on the given topic, and (3) influence each other and develop a more profound understanding of the subject than when they are isolated. While it seems that doing collaboration with others will make sense in many situations, the value of collaboration is often overlooked. Based on their extensive study with patent office workers, Hansen and Jarvelin [2] concluded that the assumption that information retrieval performance is purely individual needs to be reconsidered. Twidale and Nichols [4] argued that a truly user-centered system must acknowledge and support collaborative interactions between users and showed that users often desire to collaborate on search tasks. As the need to collaborate continues, the behavior of users in a collaborative environments remains under-studied, and the tools to facilitate collaborative information seeking have left much to be desired, my proposed research for studying Collaborative Information Seeking (CIS) is not only timely, but also very important. So far my research has primarily been focused on the system side. For instance, my work with others has shown the effectiveness of combing queries from different users for the same topic [1], as well as of algorithmically mediating the results for a collaborative search environment [3]. I would now like to focus my attention on more user-centric IR systems that facilitate intentional, interactive, and immediate collaboration among users. As a first step in this direction, I have developed a system called Coagmento, which allows a pair of users to search TREC collections and col-
How to cope with questions typed by dyslexic users
Authors: Laurianne Sitbon, Patrice Bellot
Venue: SIGIR
Year: 2008

In this paper we propose a way to cope with questions typed by dyslexic users as they are usually a deformation of the intended query that cannot be corrected with classical spellcheckers. We first propose a new model for statistic question answering systems based on a probabilistic information retrieval model and a combination of results. This model allows a multiple weighted terms query as an input. We also introduce a phonology based approach at the sentence level to derive possible intended terms from typed questions. This approach uses the finite state machine framework to go from phonetic hypothesis and spellchecker proposals to hypothesized sentences thanks to a language model. The final weighted queries are obtained thanks to posterior probabilities computation. They are evaluated according to new density and appearance rating measures which adapt recall and precision to non binary data.
ResIn: a combination of results caching and index pruning for high-performance web search engines
Authors: Gleb Skobeltsyn, Flavio Junqueira, Vassilis Plachouras, Ricardo Baeza-Yates
Venue: SIGIR
Year: 2008

Results caching is an efficient technique for reducing the query processing load, hence it is commonly used in real search engines. This technique, however, bounds the maximum hit rate due to the large fraction of singleton queries, which is an important limitation. In this paper we propose ResIn - an architecture that uses a combination of results caching and index pruning to overcome this limitation.
Searching blogs and news: a study on popular queries
Authors: Aixin Sun, Meishan Hu, Ee-Peng Lim
Venue: SIGIR
Year: 2008

Blog/news search engines are very important channels to reach information about the real-time happenings. In this paper, we study the popular queries collected over one year period and compare their search results returned by a blog search engine (i.e., Technorati) and a news search engine (i.e., Google News). We observed that the numbers of hits returned by the two search engines for the same set of queries were highly correlated, suggesting that blogs often provide commentary to current events reported in news. As many popular queries are related to some events, we further observed a high cohesiveness among the returned search results for these queries.
Noise and information
Authors: John Tait
Venue: SIGIR
Year: 2008

In this extended abstract I examine the notion of noise and its application to Information Retrieval and urge the reader to consider noise as an intrinsic property of information, not merely a problem to be eliminated.
Combining learn-based and lexicon-based techniques for sentiment detection without using labeled examples
Authors: Songbo Tan, Yuefen Wang, Xueqi Cheng
Venue: SIGIR
Year: 2008

In this work, we propose a novel scheme for sentiment classification (without labeled examples) which combines the strengths of both "learn-based" and "lexicon-based" approaches as follows: we first use a lexicon-based technique to label a portion of informative examples from given task (or domain); then learn a new supervised classifier based on these labeled ones; finally apply this classifier to the task. The experimental results indicate that proposed scheme could dramatically outperform "learn-based" and "lexicon-based" techniques.
Enhancing keyword-based botanical information retrieval with information extraction
Authors: Xiaoya Tang
Venue: SIGIR
Year: 2008

Keyword-based retrieval matches search terms and documents via term co-occurrence. Such an approach does not allow matching based on the specific plant characteristic descriptions that are often used in botanical text retrieval. This study applies information extraction techniques to automatically extract plant characteristic information from text and allows users to search using such information in combination with keywords. An evaluation experiment was conducted using actual users. The results indicate that this approach enhances task-based retrieval performance.
SpotSigs: robust and efficient near duplicate detection in large web collections
Authors: Martin Theobald, Jonathan Siddharth, Andreas Paepcke
Venue: SIGIR
Year: 2008

Motivated by our work with political scientists who need to manually analyze large Web archives of news sites, we present SpotSigs, a new algorithm for extracting and matching signatures for near duplicate detection in large Web crawls. Our spot signatures are designed to favor natural-language portions of Web pages over advertisements and navigational bars.
Generalising multiple capture-recapture to non-uniform sample sizes
Authors: Paul Thomas
Venue: SIGIR
Year: 2008

Algorithms in distributed information retrieval often rely on accurate knowledge of the size of a collection. The "multiple capture-recapture" method of Shokouhi et al. is one of the more reliable algorithms for determining collection size, but it relies on samples with a uniform number of documents. Such uniform samples are often hard to obtain in a working system.
Measuring concept relatedness using language models
Authors: Dolf Trieschnigg, Edgar Meij, Maarten de Rijke, Wessel Kraaij
Venue: SIGIR
Year: 2008

Over the years, the notion of concept relatedness has attracted considerable attention. A variety of approaches, based on ontology structure, information content, association, or context have been proposed to indicate the relatedness of abstract ideas. We propose a method based on the cross entropy reduction between language models of concepts which are estimated based on document-concept assignments. The approach shows improved or competitive results compared to state-of-the-art methods on two test sets in the biomedical domain.
Term clouds as surrogates for user generated speech
Authors: Manos Tsagkias, Martha A. Larson, Maarten de Rijke
Venue: SIGIR
Year: 2008

User generated spoken audio remains a challenge for Automatic Speech Recognition (ASR) technology and content-based audio surrogates derived from ASR-transcripts must be error robust. An investigation of the use of term clouds as surrogates for podcasts demonstrates that ASR term clouds closely approximate term clouds derived from human-generated transcripts across a range of cloud sizes. A user study confirms the conclusion that ASR-clouds are viable surrogates for depicting the content of podcasts.
Inferring the most important types of a query: a semantic approach
Authors: David Vallet, Hugo Zaragoza
Venue: SIGIR
Year: 2008

In this paper we present a technique for ranking the most important types or categories for a given query. Rather than trying to find the category of the query, known as query categorization, our approach seeks to find the most important types related to the query results. Not necessarily the query category falls into this ranking of types and therefore our approach can be complementary.
Learning to rank at query-time using association rules
Authors: Adriano Veloso, Humberto Mossri de Almeida, Marcos André Gonçalves, Wagner Meira Jr.
Venue: SIGIR
Year: 2008

Some applications have to present their results in the form of ranked lists. This is the case of many information retrieval applications, in which documents must be sorted according to their relevance to a given query. This has led the interest of the information retrieval community in methods that automatically learn effective ranking functions. In this paper we propose a novel method which uncovers patterns (or rules) in the training data associating features of the document with its relevance to the query, and then uses the discovered rules to rank documents. To address typical problems that are inherent to the utilization of association rules (such as missing rules and rule explosion), the proposed method generates rules on a demand-driven basis, at query-time. The result is an extremely fast and effective ranking method. We conducted a systematic evaluation of the proposed method using the LETOR benchmark collections. We show that generating rules on a demand-driven basis can boost ranking performance, providing gains ranging from 12 % to 123%, outperforming the state-of-the-art methods that learn to rank, with no need of time-consuming and laborious pre-processing. As a highlight, we also show that additional information, such as query terms, can make the generated rules more discriminative, further improving ranking performance.
A faceted interface for multimedia search
Authors: Robert Villa, Nicholas Gildea, Joemon M. Jose
Venue: SIGIR
Year: 2008

With the rapid increase in online video services, video retrieval systems are becoming increasingly important search tools to many users in many different fields. In this poster we present a novel video retrieval interface, which supports the creation of multiple search "facets", to aid users carrying out complex, multi-faceted search tasks. The interface allows multiple searches to be executed and viewed simultaneously, and allows material to be reorganized between the facets. An experiment is presented which compares the faceted interface to a tabbed interface similar to that on modern web browsers, and some preliminary results are given.