OpenRaiser/Intern-Atlas huggingface dataset:
Expressive Retrieval from XML Documents
Authors: Taurai Tapiwa Chinenyanga, Nicholas Kushmerick
Venue: SIGIR
Year: 2001

The emergence of XML as a standard interchange format for structured documents/data has given rise to many XML query language proposals. However, some of these languages do not support information retrieval-style ranked queries based on textual similarity. There have been several extensions to these query languages to support keyword search, but the resulting query languages cannot express queries such as``find books and CDs with similar titles''. Either these extensions use keywords as mere boolean filters, or similarities can be calculated only between data values and constants rather than two data values. We propose ELIXIR, an \textbf{\underline{e}}xpressive and \textbf{\underline{e}}fficient\textbf{\underline{l}}anguage for \textbf{\underline{X}}ML \textbf{\underline{i}}nformation \textbf{\underline{r}}etrieval that extends the query language XML-QL \cite{deutsch-www8,deutsch-deb99} with a textual similarity operator. ELIXIR is a general-purpose XML information retrieval language, sufficiently expressive to handle the above query. Our algorithm for answering ELIXIR queries rewrites the original ELIXIR query into a series of XML-QL queries that generate intermediate relational data, and uses relational database techniques to efficiently evaluate the similarity operators on this intermediate data, yielding an XML document with nodes ranked by similarity. Our experiments demonstrate that our prototype scales well with the size of the XML data and complexity of the query.
Probabilistic Combination of Content and Links
Authors: Susan T. Dumais, Rong Jin
Venue: SIGIR
Year: 2001

Previous research has shown that citations and hypertext links can be usefully combined with document content to improve retrieval. Links can be used in many ways, e.g., link topology can be used to identify important pages, anchor text can be used to augment the text of cited pages, and activation can be spread to linked pages. This paper introduces a probabilistic model that integrates content matching and these three uses of link information in a single unified framework. Experiments with a web collection show benefits for link information especially for general queries.
Building Interoperable Digital Library Services: MARIAN, Open Archives and NDLTD
Authors: Edward A. Fox, Robert K. France, Marcos André Gonçalves, Hussein Suleman
Venue: SIGIR
Year: 2001

In this demonstration, we present interoperable and personalized search services for the Networked Digital Library of Theses and Dissertations (NDLTD). Using standard protocols and software, including those specified by the Open Archives Initiative (OAI), distributed sites can share metadata easily. On top of these harvesting protocols, we implement a union collection of theses managed by the MARIAN digital library system. Our demonstration covers aspects of NDLTD, OAI, and MARIAN.
Unsupervised and Supervised Clustering for Topic Tracking
Authors: Martin Franz, J. Scott McCarley, Todd Ward, Wei-Jing Zhu
Venue: SIGIR
Year: 2001

We investigate important differences between two styles of document clustering in the context of Topic Detection and Tracking. Converting a Topic Detection system into a Topic Tracking system exposes fundamental differences between these two tasks that are important to consider in both the design and the evaluation of TDT systems. We also identify features that can be used in systems for both tasks.
Quantifying the Utility of Parallel Corpora
Authors: Martin Franz, J. Scott McCarley, Todd Ward, Wei-Jing Zhu
Venue: SIGIR
Year: 2001

Our English-Chinese cross-language IR system is trained from parallel corpora; we investigate its performance as a function of training corpus size for three different training corpora. We find that the performance of the system as trained on the three parallel corpora can be related by a simple measure, namely the out-of-vocabulary rate of query words.
XIRQL: A Query Language for Information Retrieval in XML Documents
Authors: Norbert Fuhr, Kai Großjohann
Venue: SIGIR
Year: 2001

Based on the document-centric view of XML, we present the query language XIRQL. Current proposals for XML query languages lack most IR-related features, which are weighting and ranking, relevance-oriented search, datatypes with vague predicates, and semantic relativism. XIRQL integrates these features by using ideas from logic-based probabilistic IR models, in combination with concepts from the database area. For processing XIRQL queries, a path algebra is presented, that also serves as a starting point for query optimization.
Improving Query Translation for Cross-Language Information Retrieval Using Statistical Models
Authors: Jianfeng Gao, Endong Xun, Ming Zhou, Changning Huang, Jian-Yun Nie, Jian Zhang
Venue: SIGIR
Year: 2001

Dictionaries have often been used for query translation in cross-language information retrieval (CLIR). However, we are faced with the problem of translation ambiguity, i.e. multiple translations are stored in a dictionary for a word. In addition, a word-by-word query translation is not precise enough. In this paper, we explore several methods to improve the previous dictionary-based query translation. First, as many as possible, noun phrases are recognized and translated as a whole by using statistical models and phrase translation patterns. Second, the best word translations are selected based on the cohesion of the translation words. Our experimental results on TREC English-Chinese CLIR collection show that these techniques result in significant improvements over the simple dictionary approaches, and achieve even better performance than a high-quality machine translation system.
Automatic Web Search Query Generation to Create Minority Language Corpora
Authors: Rayid Ghani, Rosie Jones, Dunja Mladenic
Venue: SIGIR
Year: 2001

The Web is a valuable source of language specific resources but collecting, organizing and utilizing this information is difficult. We describe CorpusBuilder, an approach for automatically generating Web-search queries to collect documents in a minority language. It differs from pseudo-relevance feedback in that retrieved documents are labeled by an automatic language classifier as relevant or irrelevant and a subset of documents is used to generate new queries. We experiment with various query-generation methods and query-lengths to find inclusion/exclusion terms that are helpful for finding documents in the target language and find that using odds-ratio scores calculated over the documents acquired so far was one of the most consistently accurate query-generation methods. We also describe experiments using a handful of words elicited from a user instead of initial documents and show that the methods perform similarly. Applying the same approach to multiple languages show that our system generalizes to a variety of languages.
Query Optimization for Vector Space Problems
Authors: Kazuo Goda, Masaru Kitsuregawa, Takayuki Tamura, Ophir Frieder, Abdur Chowdhury
Venue: SIGIR
Year: 2001

We present performance measurement results for a parallel SQL based information retrieval system implemented on a PC cluster system. We used the Web-TREC dataset under a left-deep query execution plan. We achieved satisfactory speed up.
Improving Cross Language Information Retrieval with Triangulated Translation
Authors: Tim Gollins, Mark Sanderson
Venue: SIGIR
Year: 2001

Most approaches to cross language information retrieval assume that resources providing a direct translation between the query and document languages exist. This paper presents research examining the situation where such an assumption is false. Here, an intermediate (or pivot) language provides a means of transitive translation of the query language to that of the document via the pivot, at the cost, however, of introducing much error. The paper reports the novel approach of translating in parallel across multiple intermediate languages and fusing the results. Such a technique removes the error, raising the effectiveness of the tested retrieval system, up to and possibly above the level expected, had a direct translation route existed. Across a number of retrieval situations and combinations of languages, the approach proves to be highly effective.
Generic Text Summarization Using Relevance Measure and Latent Semantic Analysis
Authors: Yihong Gong, Xin Liu
Venue: SIGIR
Year: 2001

In this paper, we propose two generic text summarization methods that create text summaries by ranking and extracting sentences from the original documents. The first method uses standard IR methods to rank sentence relevances, while the second method uses the latent semantic analysis technique to identify semantically important sentences, for summary creations. Both methods strive to select sentences that are highly ranked and different from each other. This is an attempt to create a summary with a wider coverage of the document's main content and less redundancy. Performance evaluations on the two summarization methods are conducted by comparing their summarization outputs with the manual summaries generated by three independent human evaluators. The evaluations also study the influence of different VSM weighting schemes on the text summarization performances. Finally, the causes of the large disparities in the evaluators' manual summarization results are investigated, and discussions on human text summarization patterns are presented.
Toward an Improved Concept-Based Information Retrieval System
Authors: Peter V. Henstock, Young-Suk Lee, Clifford J. Weinstein, Daniel J. Pack
Venue: SIGIR
Year: 2001

This paper presents a novel information retrieval system that includes 1) the addition of concepts to facilitate the identification of the correct word sense, 2) a natural language query interface, 3) the inclusion of weights and penalties for proper nouns that build upon the Okapi weighting scheme, and 4) a term clustering technique that exploits the spatial proximity of search terms in a document to further improve the performance. The effectiveness of the system is validated by experimental results.
Unitary Operators for Fast Latent Semantic Indexing (FLSI)
Authors: Eduard Hoenkamp
Venue: SIGIR
Year: 2001

Latent Semantic Indexing (LSI) dramatically reduces the dimension of the document space by mapping it into a space spanned by conceptual indices. Empirically, the number of concepts that can represent the documents are far fewer than the great variety of words in the textual representation. Although this almost obviates the problem of lexical matching, the mapping incurs a high computational cost compared to document parsing, indexing, query matching, and updating. This paper shows how LSI is based on a unitary transformation, for which there are computationally more attractive alternatives. This is exemplified by the Haar transform, which is memory efficient, and can be computed in linear to sublinear time. The principle advantages of LSI are thus preserved while the computational costs are drastically reduced.
Meta-scoring: Automatically Evaluating Term Weighting Schemes in IR without Precision-Recall
Authors: Rong Jin, Christos Falusos, Alexander G. Hauptmann
Venue: SIGIR
Year: 2001

In this paper, we present a method that can automatically evaluate performance of different term weighting schemes in information retrieval without resorting to precision-recall based on human relevance judgments. Specifically, the problem is: given two document-term matrixes generated from two different term weighting schemes, can we tell which term weighting scheme will performance better than the other? We propose a meta-scoring function, which takes as input the document-term matrix generated by some term weighting scheme and computes a goodness score from the document-term matrix. In our experiments, we found out that this score is highly correlated with the precision-recall measurement for all the collections and term weighting schema we tried. Thus, we conclude that our meta-scoring function can be a substitute for the precision-recall measurement that needs relevance judgments of human subject. Furthermore, this meta-scoring function is not limited only to text information retrieval can be applied to fields such as image and DNA retrieval.
A Statistical Learning Model of Text Classification for Support Vector Machines
Authors: Thorsten Joachims
Venue: SIGIR
Year: 2001

This paper develops a theoretical learning model of text classification for Support Vector Machines (SVMs). It connects the statistical properties of text-classification tasks with the generalization performance of a SVM in a quantitative way. Unlike conventional approaches to learning text classifiers, which rely primarily on empirical evidence, this model explains why and when SVMs perform well for text classification. In particular, it addresses the following questions: Why can support vector machines handle the large feature spaces in text classification effectively? How is this related to the statistical properties of text? What are sufficient conditions for applying SVMs to text-classification problems successfully?
Document Language Models, Query Models, and Risk Minimization for Information Retrieval
Authors: John D. Lafferty, ChengXiang Zhai
Venue: SIGIR
Year: 2001

We present a framework for information retrieval that combines document models and query models using a probabilistic ranking function based on Bayesian decision theory. The framework suggests an operational retrieval model that extends recent developments in the language modeling approach to information retrieval. A language model for each document is estimated, as well as a language model for each query, and the retrieval problem is cast in terms of risk minimization. The query language model can be exploited to model user preferences, the context of a query, synonomy and word senses. While recent work has incorporated word translation models for this purpose, we introduce a new method using Markov chains defined on a set of documents to estimate the query models. The Markov chain method has connections to algorithms from link analysis and social networks. The new approach is evaluated on TREC collections and compared to the basic language modeling approach and vector space models together with query expansion using Rocchio. Significant improvements are obtained over standard query expansion methods for strong baseline TF-IDF systems, with the greatest improvements attained for short queries on Web data.
A Meta-Learning Approach for Text Categorization
Authors: Wai Lam, Kwok-Yin Lai
Venue: SIGIR
Year: 2001

We investigate a meta-model approach, called Meta-learning Using Document Feature characteristics (MUDOF), for the task of automatic textual document categorization. It employs a meta-learning phase using document feature characteristics. Document feature characteristics, derived from the training document set, capture some inherent category-specific properties of a particular category. Different from existing categorization methods, MUDOF can automatically recommend a suitable algorithm for each category based on the category-specific statistical characteristics. Hence, different algorithms may be employed for different categories. Experiments have been conducted on a real-world document collection demonstrating the effectiveness of our approach. The results confirm that our meta-model approach can exploit the advantage of its component algorithms, and demonstrate a better performance than existing algorithms.
Distributed Resource Discovery and Structured Data Searching with Cheshire II
Authors: Ray R. Larson
Venue: SIGIR
Year: 2001

This demonstration will show describe the construction and application of Cross-Domain Information Servers using features of the standard Z39.50 information retrieval protocol[Z39.50]. The system is currently being used to build and search distributed indexes for databases with disparate structured data (SGML and XML). We use the Z39.50 Explain Database to determine the databases and indexes of a given server, then use the Z39.50 SCAN facility to extract the contents of the indexes. This information is used to build collection documents that can be retrieved using probabilistic retrieval algorithms.
Intelligent Object-based Image Retrieval Using Cluster-driven Personal Preference Learning
Authors: Kyoung-Mi Lee, W. Nick Street
Venue: SIGIR
Year: 2001

This paper introduces a personalization method for image retrieval based on the learning of personal preferences. The proposed system indexes objects based on shape and groups them into a set of clusters, or prototypes. Our personalization method refines corresponding prototypes from objects provided by the user in the foreground, and simultaneously adapts the database index in the background.
Perceptual Consistency Improves Image Retrieval Performance
Authors: Huizhong Long, Wee Kheng Leow
Venue: SIGIR
Year: 2001

An ideal retrieval system should retrieve images that satisfy the user's need, and should, therefore, measure image similarity in a manner consistent with human's perception. However, existing computational similarity measures are not perceptually consistent. This paper proposes an approach of improving retrieval performance by improving the perceptual consistency of computational similarity measures for textures based on relevance feedback judgments.
An Homogeneous Framework to Model Relevance Feedback
Authors: David E. Losada, Alvaro Barreiro
Venue: SIGIR
Year: 2001

Relevance feedback is an appreciated process to produce increasingly better retrieval. Usually, positive feedback plays a fundamental role in the feedback process whereas the role of negative feedback is limited. We think that negative feedback is a promising precision oriented mechanism and we propose a logical framework in which positive and negative feedback are homogeneously modeled. Evaluation results against small test collections are provided.
Anchor Text Mining for Translation Extraction of Query Terms
Authors: Wen-Hsiang Lu, Hsi-Jian Lee, Lee-Feng Chien
Venue: SIGIR
Year: 2001

This paper presents an approach to automatically extracting the bilingual translations of many Web query terms through mining the Web anchor texts. Some preliminary experiments are conducted on using 109,416 Web pages containing both Chinese and English anchor texts in their in-links to extract Chinese translations of 200 English queries selected from popular query terms in Taiwan. It is found that the effective translations of 75% of the popular query terms can be extracted, in which 87.2% cannot be obtained in common translation dictionaries.
Intelligent Information Triage
Authors: Sofus A. Macskassy, Haym Hirsh, Foster J. Provost, Ramesh Sankaranarayanan, Vasant Dhar
Venue: SIGIR
Year: 2001

In many applications, large volumes of time-sensitive textual information require triage: rapid, approximate prioritization for subsequent action. In this paper, we explore the use of prospective indications of the importance of a time-sensitive document, for the purpose of producing better document filtering or ranking. By prospective, we mean importance that could be assessed by actions that occur in the future. For example, a news story may be assessed (retrospectively) as being important, based on events that occurred after the story appeared, such as a stock price plummeting or the issuance of many follow-up stories. If a system could anticipate (prospectively) such occurrences, it could provide a timely indication of importance. Clearly, perfect prescience is impossible. However, sometimes there is sufficient correlation between the content of an information item and the events that occur subsequently. We describe a process for creating and evaluating approximate information-triage procedures that are based on prospective indications. Unlike many information-retrieval applications for which document labeling is a laborious, manual process, for many prospective criteria it is possible to build very large, labeled, training corpora automatically. Such corpora can be used to train text classification procedures that will predict the (prospective) importance of each document. This paper illustrates the process with two case studies, demonstrating the ability to predict whether a news story will be followed by many, very similar news stories, and also whether the stock price of one or more companies associated with a news story will move significantly following the appearance of that story. We conclude by discussing how the comprehensibility of the learned classifiers can be critical to success.}
Evaluating a Content Based Image Retrieval System
Authors: Sharon McDonald, John Tait, Ting-Sheng Lai
Venue: SIGIR
Year: 2001

Content Based Image Retrieval (CBIR) presents special challenges in terms of how image data is indexed, accessed, and how end systems are evaluated. This paper discusses the design of a CBIR system that uses global colour as the primary indexing key, and a user centered evaluation of the systems visual search tools. The results indicate that users are able to make use of a range of visual search tools, and that different tools are used at different points in the search process. The results also show that the provision of a structured navigation and browsing tool can support image retrieval, particularly in situations in which the user does not have a target image in mind. The results are discussed in terms of their implications for the design of visual search tools, and their implications for the use of user-centered evaluation for CBIR systems.
Evaluating Topic-Driven Web Crawlers
Authors: Filippo Menczer, Gautam Pant, Padmini Srinivasan, Miguel E. Ruiz
Venue: SIGIR
Year: 2001

Due to limited bandwidth, storage, and computational resources, and to the dynamic nature of the Web, search engines cannot index every Web page, and even the covered portion of the Web cannot be monitored continuously for changes. Therefore it is essential to develop effective crawling strategies to prioritize the pages to be indexed. The issue is even more important for topic-specific search engines, where crawlers must make additional decisions based on the relevance of visited pages. However, it is difficult to evaluate alternative crawling strategies because relevant sets are unknown and the search space is changing. We propose three different methods to evaluate crawling strategies. We apply the proposed metrics to compare three topic-driven crawling algorithms based on similarity ranking, link analysis, and adaptive agents.
MS Read: User Modeling in the Web Environment
Authors: Natasa Milic-Frayling, Ralph Sommerer
Venue: SIGIR
Year: 2001

MS Read is a prototype application implemented as an extension of the Web Browser that creates an evolving model of the users topic of interest. It uses that model to analyze documents that are accessed while searching and browsing the Web. In the presented version of MS Read the model is used to highlight topic related terminology in the documents.
Transparent Queries: Investigating Users' Mental Models of Search Engines
Authors: Jack Muramatsu, Wanda Pratt
Venue: SIGIR
Year: 2001

Typically, commercial Web search engines provide very little feedback to the user concerning how a particular query is processed and interpreted. Specifically, they apply key query transformations without the users knowledge. Although these transformations have a pronounced effect on query results, users have very few resources for recognizing their existence and understanding their practical importance. We conducted a user study to gain a better understanding of users knowledge of and reactions to the operation of several query transformations that web search engines automatically employ. Additionally, we developed and evaluated Transparent Queries, a software system designed to provide users with lightweight feedback about opaque query transformations. The results of the study suggest that users do indeed have difficulties understanding the operation of query transformations without additional assistance. Finally, although transparency is helpful and valuable, interfaces that allow direct control of query transformations might ultimately be more helpful for end-users.