OpenRaiser/Intern-Atlas huggingface dataset:
Mining multimedia salient concepts for incremental information extraction
Authors: João Magalhães, Stefan M. Rüger
Venue: SIGIR
Year: 2005

We propose a novel algorithm for extracting information by mining the feature space clusters and then assigning salient concepts to them. Bayesian techniques for extracting concepts from multimedia usually suffer either from lack of data or from too complex concepts to be represented by a single statistical model. An incremental information extraction approach, working at different levels of abstraction, would be able to handle concepts of varying complexities. We present the results of our research on the initial part of an incremental approach, the extraction of the most salient concepts from multimedia information.
Bootstrapping dictionaries for cross-language information retrieval
Authors: Kornél G. Markó, Stefan Schulz, Olena Medelyan, Udo Hahn
Venue: SIGIR
Year: 2005

The bottleneck for dictionary-based cross-language information retrieval is the lack of comprehensive dictionaries, in particular for many different languages. We here introduce a methodology by which multilingual dictionaries (for Spanish and Swedish) emerge automatically from simple seed lexicons. These seed lexicons are automatically generated, by cognate mapping, from (previously manually constructed) Portuguese and German as well as English sources. Lexical and semantic hypotheses are then validated and new ones iteratively generated by making use of co-occurrence patterns of hypothesized translation synonyms in parallel corpora. We evaluate these newly derived dictionaries on a large medical document collection within a cross-language retrieval setting.
Web-based acquisition of Japanese katakana variants
Authors: Takeshi Masuyama, Hiroshi Nakagawa
Venue: SIGIR
Year: 2005

This paper describes a method of detecting Japanese Katakana variants from a large corpus. Katakana words, which are mainly used as loanwords, cause problems with information retrieval and so on, because transliteration creates several variations in spelling and all of these can be orthographic. Previous works manually defined Katakana rewrite rules such as %Y (be) and %t%' (ve) being replaceable with each other, for generating variants and also defined the weight of each operation to edit one string into another to detect these variants. However, these previous researches have not been able to keep up with the ever-increasing number of loanwords and their variants. With our method proposed in this paper, the weight of each edit operation is mechanically assigned based on Web data. In experiments, it performed almost as well as one with manually determined weights. Thus, the advantages of our method are: 1) need no expertise in linguistics to determine weight of each operation, and 2) able to keep up with new Katakana loanwords only by collecting text data from Web and acquiring new weights of edit operations automatically. It also achieved 98.6% recall and 86.3% precision in the task of extracting Katakana variant pairs from 38 year's worth of corpora of Japanese newspaper articles.
Do summaries help?
Authors: Kathleen R. McKeown, Rebecca J. Passonneau, David K. Elson, Ani Nenkova, Julia Hirschberg
Venue: SIGIR
Year: 2005

We describe a task-based evaluation to determine whether multi-document summaries measurably improve user performance whe using online news browsing systems for directed research. We evaluated the multi-document summaries generated by Newsblaster, a robust news browsing system that clusters online news articles and summarizes multiple articles on each event. Four groups of subjects were asked to perform the same time-restricted fact-gathering tasks, reading news under different conditions: no summaries at all, single sentence summaries drawn from one of the articles, Newsblaster multi-document summaries, and human summaries. Our results show that, in comparison to source documents only, the quality of reports assembled using Newsblaster summaries was significantly better and user satisfaction was higher with both Newsblaster and human summaries.
A Markov random field model for term dependencies
Authors: Donald Metzler, W. Bruce Croft
Venue: SIGIR
Year: 2005

This paper develops a general, formal framework for modeling term dependencies via Markov random fields. The model allows for arbitrary text features to be incorporated as evidence. In particular, we make use of features based on occurrences of single terms, ordered phrases, and unordered phrases. We explore full independence, sequential dependence, and full dependence variants of the model. A novel approach is developed to train the model that directly maximizes the mean average precision rather than maximizing the likelihood of the training data. Ad hoc retrieval experiments are presented on several newswire and web collections, including the GOV2 collection used at the TREC 2004 Terabyte Track. The results show significant improvements are possible by modeling dependencies, especially on the larger web collections.
Iterative translation disambiguation for cross-language information retrieval
Authors: Christof Monz, Bonnie J. Dorr
Venue: SIGIR
Year: 2005

Finding a proper distribution of translation probabilities is one of the most important factors impacting the effectiveness of a cross-language information retrieval system. In this paper we present a new approach that computes translation probabilities for a given query by using only a bilingual dictionary and a monolingual corpus in the target language. The algorithm combines term association measures with an iterative machine learning approach based on expectation maximization. Our approach considers only pairs of translation candidates and is therefore less sensitive to data-sparseness issues than approaches using higher n-grams. The learned translation probabilities are used as query term weights and integrated into a vector-space retrieval system. Results for English-German cross-lingual retrieval show substantial improvements over a baseline using dictionary lookup without term weighting.
A temporally adaptive content-based relevance ranking algorithm
Authors: Jukka Perkiö, Wray L. Buntine, Henry Tirri
Venue: SIGIR
Year: 2005

In information retrieval relevance ranking of the results is one of the most important single tasks there are. There are many diffierent ranking algorithms based on the content of the documents or on some external properties e.g. link structure of html documents.We present a temporally adaptive content-based relevance ranking algorithm that explicitly takes into account the temporal behavior of the underlying statistical properties of the documents in the form of a statistical topic model. more we state that our algorithm can be used on top of any ranking algorithm.
The Portinari project: IR helps art and culture
Authors: João Candido Portinari
Venue: SIGIR
Year: 2005

In May 30, 1983, The New York Times published the article "Brazil Gathers Archive On Its Painter, Portinari". The author, Warren Hoge, narrates: "/The late Candido Portinari is considered here to be the greatest artist Brazil has ever produced, yet all but a few of his 4,000 paintings are out of public view. They have become dispersed in private collections in so many places that his biographer compared their fate to that of Brazil's 18th-century revolutionary hero Tiradentes, whose body was dismembered and strewn along a 300-mile turnpike. The inaccessibility of Portinari's work is particularly vexing to his enthusiasts because his own dedication to producing an epic view of Brazil for his countrymen was such that he continued painting even after doctors warned that exposure to paint was killing him. He died of lead poisoning at the age of 58. Now, in a pioneering effort for Latin America, a team of experts in Rio is busy assembling the far-flung pieces of Portinari's obra into an exhaustive computerized archive. "/We are trying to rescue what is authentically ours'/, said João Candido Portinari, the painter's 44-year-old son, who is the coordinator of the group of researchers who make their headquarters on the leafy campus of Rio's Pontifical Catholic University. A telecommunications engineer with a Ph.D. from the Massachusetts Institute of Technology and a former chairman of the university's mathematics department, Mr. Portinari has brought exacting technical standards to the task. Now four years into the project, the 14-member team has compiled photographic, technical.
Combining eye movements and collaborative filtering for proactive information retrieval
Authors: Kai Puolamäki, Jarkko Salojärvi, Eerika Savia, Jaana Simola, Samuel Kaski
Venue: SIGIR
Year: 2005

We study a new task, proactive information retrieval by combining implicit relevance feedback and collaborative filtering. We have constructed a controlled experimental setting, a prototype application, in which the users try to find interesting scientific articles by browsing their titles. Implicit feedback is inferred from eye movement signals, with discriminative hidden Markov models estimated from existing data in which explicit relevance feedback is available. Collaborative filtering is carried out using the User Rating Profile model, a state-of-the-art probabilistic latent variable model, computed using Markov Chain Monte Carlo techniques. For new document titles the prediction accuracy with eye movements, collaborative filtering, and their combination was significantly better than by chance. The best prediction accuracy still leaves room for improvement but shows that proactive information retrieval and combination of many sources of relevance feedback is feasible.
Hierarchical text summarization for WAP-enabled mobile devices
Authors: Dragomir R. Radev, Omer Kareem, Jahna Otterbacher
Venue: SIGIR
Year: 2005

We present WAP MEAD, a WAP-enabled text summarization system. It incorporates a state-of-the art text summarizer enhanced to produce hierarchical summaries that are appropriate for various types of mobile devices, including cellular phones.
An application of text categorization methods to gene ontology annotation
Authors: Kazuhiro Seki, Javed Mostafa
Venue: SIGIR
Year: 2005

This paper describes an application of IR and text categorization methods to a highly practical problem in biomedicine, specifically, Gene Ontology (GO) annotation. GO annotation is a major activity in most model organism database projects and annotates gene functions using a controlled vocabulary. As a first step toward automatic GO annotation, we aim to assign GO domain codes given a specific gene and an article in which the gene appears, which is one of the task challenges at the TREC 2004 Genomics Track. We approached the task with careful consideration of the specialized terminology and paid special attention to dealing with various forms of gene synonyms, so as to exhaustively locate the occurrences of the target gene. We extracted the words around the gene occurrences and used them to represent the gene for GO domain code annotation. As a classifier, we adopted a variant of k-Nearest Neighbor (kNN) with supervised term weighting schemes to improve the performance, making our method among the top-performing systems in the TREC official evaluation. Moreover, it is demonstrated that our proposed framework is successfully applied to another task of the Genomics Track, showing comparable results to the best performing system.
Automated evaluation of search engine performance via implicit user feedback
Authors: Himanshu Sharma, Bernard J. Jansen
Venue: SIGIR
Year: 2005

Measuring the information retrieval effectiveness of Web search engines can be expensive if human relevance judgments are required to evaluate search results. Using implicit user feedback for search engine evaluation provides a cost and time effective manner of addressing this problem. Web search engines can use human evaluation of search results without the expense of human evaluators. An additional advantage of this approach is the availability of real time data regarding system performance. Wecapture user relevance judgments actions such as print, save and bookmark, sending these actions and the corresponding document identifiers to a central server via a client application. We use this implicit feedback to calculate performance metrics, such as precision. We can calculate an overall system performance metric based on a collection of weighted metrics.
Active feedback in ad hoc information retrieval
Authors: Xuehua Shen, ChengXiang Zhai
Venue: SIGIR
Year: 2005

Information retrieval is, in general, an iterative search process, in which the user often has several interactions with a retrieval system for an information need. The retrieval system can actively probe a user with questions to clarify the information need instead of just passively responding to user queries. A basic question is thus how a retrieval system should propose questions to the user so that it can obtain maximum benefits from the feedback on these questions. In this paper, we study how a retrieval system can perform active feedback, i.e., how to choose documents for relevance feedback so that the system can learn most from the feedback information. We present a general framework for such an active feedback problem, and derive several practical algorithms as special cases. Empirical evaluation of these algorithms shows that the performance of traditional relevance feedback (presenting the top K documents) is consistently worse than that of presenting documents with more diversity. With a diversity-based selection algorithm, we obtain fewer relevant documents, however, these fewer documents have more learning benefits.
A web mining research platform
Authors: David Sherfesee, Niall O&apos, Driscoll
Venue: SIGIR
Year: 2005

We demonstrate the Alexa Web Mining Platform, a data mining and web service publication platform designed to enable analysis of Alexa's massive web data store. The system provides researchers and developers high speed access to our web crawl, crawl metadata, long term storage, and data publication utilities. We demonstrate the system's capabilities and user interface.
Gravitation-based model for information retrieval
Authors: Shuming Shi, Ji-Rong Wen, Qing Yu, Ruihua Song, Wei-Ying Ma
Venue: SIGIR
Year: 2005

This paper proposes GBM (gravitation-based model), a physical model for information retrieval inspired by Newton's theory of gravitation. A mapping is built in this model from concepts of information retrieval (documents, queries, relevance, etc) to those of physics (mass, distance, radius, attractive force, etc). This model actually provides a new perspective on IR problems. A family of effective term weighting functions can be derived from it, including the well-known BM25 formula. This model has some advantages over most existing ones: First, because it is directly based on basic physical laws, the derived formulas and algorithms can have their explicit physical interpretation. Second, the ranking formulas derived from this model satisfy more intuitive heuristics than most of existing ones, thus have the potential to behave empirically better and to be used safely on various settings. Finally, a new approach for structured document retrieval derived from this model is more reasonable and behaves better than existing ones.
Challenges in running a commercial search engine
Authors: Amit Singhal
Venue: SIGIR
Year: 2005

These are exciting times for Information Retrieval. Web search engines have brought IR to the masses. It now affects the lives of hundreds of millions of people, and growing, as Internet search companies launch ever more products based on techniques developed in These are exciting times for Information Retrieval. Web search engines have brought IR to the masses. It now affects the lives of hundreds of millions of people, and growing, as Internet search companies launch ever more products based on techniques developed in IR research.The real world poses unique challenges for search algorithms. They operate at unprecedented scales, and over a wide diversity of information. In addition, we have entered an unprecedented world of "Adversarial Information Retrieval". The lure of billions of dollars of commerce, guided by search engines, motivates all kinds of people to try all kinds of tricks to get their sites to the top of the search results.What techniques do people use to defeat IR algorithms? What are the evaluation challenges for a web search engine? How much impact has IR had on search engines? How does Google serve over 250 Million queries a day, often with sub-second response times? This talk will show that the world of algorithm and system design for commercial search engines can be described by two of Murphy's Laws: a) If anything can go wrong, it will, and b) If anything cannot go wrong, it will anyway.
Optimization strategies for complex queries
Authors: Trevor Strohman, Howard R. Turtle, W. Bruce Croft
Venue: SIGIR
Year: 2005

Previous research into the efficiency of text retrieval systems has dealt primarily with methods that consider inverted lists in sequence; these methods are known as term-at-a-time methods. However, the literature for optimizing document-at-a-time systems remains sparse.We present an improvement to the max_score optimization, which is the most efficient known document-at-a-time scoring method. Like max_score, our technique, called term bounded max_score, is guaranteed to return exactly the same scores and documents as an unoptimized evaluation, which is particularly useful for query model research. We simulated our technique to explore the problem space, then implemented it in Indri, our large scale language modeling search engine. Tests with the GOV2 corpus on title queries show our method to be 23% faster than max_score alone, and 61% faster than our document-at-a-time baseline. Our optimized query times are competitive with conventional term-at-a-time systems on this year's TREC Terabyte task.
Using dragpushing to refine centroid text classifiers
Authors: Songbo Tan, Xueqi Cheng, Bin Wang, Hongbo Xu, Moustafa Ghanem, Yike Guo
Venue: SIGIR
Year: 2005

We present a novel algorithm, DragPushing, for automatic text classification. Using a training data set, the algorithm first calculates the prototype vectors, or centroids, for each of the available document classes. Using misclassified examples, it then iteratively refines these centroids; by dragging the centroid of a correct class towards a misclassified example and in the same time pushing the centroid of an incorrect class away from the misclassified example. The algorithm is simple to implement and is computationally very efficient. Evaluation experiments conducted on two benchmark collections show that its classification accuracy is comparable to that of more complex methods, such as support vector machines (SVM).
Efficient and self-tuning incremental query expansion for top-k query processing
Authors: Martin Theobald, Ralf Schenkel, Gerhard Weikum
Venue: SIGIR
Year: 2005

We present a novel approach for efficient and self-tuning query expansion that is embedded into a top-k query processor with candidate pruning. Traditional query expansion methods select expansion terms whose thematic similarity to the original query terms is above some specified threshold, thus generating a disjunctive query with much higher dimensionality. This poses three major problems: 1) the need for hand-tuning the expansion threshold, 2) the potential topic dilution with overly aggressive expansion, and 3) the drastically increased execution cost of a high-dimensional query. The method developed in this paper addresses all three problems by dynamically and incrementally merging the inverted lists for the potential expansion terms with the lists for the original query terms. A priority queue is used for maintaining result candidates, the pruning of candidates is based on Fagin's family of top-k algorithms, and optionally probabilistic estimators of candidate scores can be used for additional pruning. Experiments on the TREC collections for the 2004 Robust and Terabyte tracks demonstrate the increased efficiency, effectiveness, and scalability of our approach.
Scalable hierarchical topic detection: exploring a sample based approach
Authors: Dolf Trieschnigg, Wessel Kraaij
Venue: SIGIR
Year: 2005

Hierarchical topic detection is a new task in the TDT 2004 evaluation program, which aims to organize an unstructured news collection in a directed acyclic graph (DAG) structure, reflecting the topics discussed. We present a scalable architecture for HTD and compare several alternative choices for agglomerative clustering and DAG optimization in order to minimize the HTD cost metric.
Publish/subscribe functionality in IR environments using structured overlay networks
Authors: Christos Tryfonopoulos, Stratos Idreos, Manolis Koubarakis
Venue: SIGIR
Year: 2005

We study the problem of offering publish/subscribe functionality on top of structured overlay networks using data models and languages from IR. We show how to achieve this by extending the distributed hash table Chord and present a detailed experimental evaluation of our proposals.
Relevance information: a loss of entropy but a gain for IDF?
Authors: Arjen P. de Vries, Thomas Rölleke
Venue: SIGIR
Year: 2005

When investigating alternative estimates for term discriminativeness, we discovered that relevance information and idf are much closer related than formulated in classical literature. Therefore, we revisited the justification of idf as it follows from the binary independent retrieval (BIR) model. The main result is a formal framework uncovering the close relationship of a generalised idf and the BIR model. The framework makes explicit how to incorporate relevance information into any retrieval function that involves an idf-component.In addition to the idf-based formulation of the BIR model, we propose Poisson-based estimates as an alternative to the classical estimates, this being motivated by the superiority of Poisson-based estimates for the within-document term frequencies. The main experimental finding is that a Poisson-based idf is superior to the classical idf, where the superiority is particularly evident for long queries.
Self-organizing distributed collaborative filtering
Authors: Jun Wang, Marcel J. T. Reinders, Reginald L. Lagendijk, Johan A. Pouwelse
Venue: SIGIR
Year: 2005

We propose a fully decentralized collaborative filtering approach that is self-organizing and operates in a distributed way. The relevances between downloading files (items) are stored locally at these items in so called item-based buddy tables and are updated each time that the items are downloaded. We then propose to use the language model to build recommendations for the different users based on the buddy tables of those items a user has downloaded previously. We have tested and compared our distributed collaborative filtering approach to centralized collaborative filtering and showed that it has similar performance. It is therefore a promising technique to facilitate recommendations in peer-to-peer networks. © 2005 ACM.
Dirichlet PageRank
Authors: Xuanhui Wang, Azadeh Shakery, Tao Tao
Venue: SIGIR
Year: 2005

PageRank has been known to be a successful algorithm in ranking web sources. In order to avoid the rank sink problem, PageRank assumes that a surfer, being in a page, jumps to a random page with a certain probability. In the standard PageRank algorithm, the jumping probabilities are assumed to be the same for all the pages, regardless of the page properties. This is not the case in the real world, since presumably a surfer would more likely follow the out-links of a high-quality hub page than follow the links of a low-quality one. In this poster, we propose a novel algorithm "Dirichlet PageRank" to address this problem by adapting exible jumping probabilities based on the number of out-links in a page. Empirical results on TREC data show that our method outperforms the standard PageRank algorithm.