Amati / Carpineto / Romano Advances in Information Retrieval

29th European Conference on IR Research, ECIR 2007, Rome, Italy, April 2-5, 2007, Proceedings
2007
ISBN: 978-3-540-71496-5
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 4425, 779 Seiten, eBook

Reihe: Lecture Notes in Computer Science

This book constitutes the refereed proceedings of the 29th annual European Conference on Information Retrieval Research, ECIR 2007, held in Rome, Italy in April 2007. The papers are organized in topical sections on theory and design, efficiency, peer-to-peer networks, result merging, queries, relevance feedback, evaluation, classification and clustering, filtering, topic identification, expert finding, XML IR, Web IR, and multimedia IR.

Zielgruppe

Research

Autoren/Hrsg.

Amati, Giambattista

Carpineto, Claudio

Romano, Giovanni

Inhaltsverzeichnis

Keynote Talks.- The Next Generation Web Search and the Demise of the Classic IR Model.- The Last Half-Century: A Perspective on Experimentation in Information Retrieval.- Learning in Hyperlinked Environments.- Theory and Design.- A Parameterised Search System.- Similarity Measures for Short Segments of Text.- Multinomial Randomness Models for Retrieval with Document Fields.- On Score Distributions and Relevance.- Modeling Term Associations for Ad-Hoc Retrieval Performance Within Language Modeling Framework.- Efficiency.- Static Pruning of Terms in Inverted Files.- Efficient Indexing of Versioned Document Sequences.- Light Syntactically-Based Index Pruning for Information Retrieval.- Sorting Out the Document Identifier Assignment Problem.- Efficient Construction of FM-index Using Overlapping Block Processing for Large Scale Texts.- Peer-to-Peer Networks (In Memory of Henrik Nottelmann).- Performance Comparison of Clustered and Replicated Information Retrieval Systems.- A Study of a Weighting Scheme for Information Retrieval in Hierarchical Peer-to-Peer Networks.- A Decision-Theoretic Model for Decentralised Query Routing in Hierarchical Peer-to-Peer Networks.- Central-Rank-Based Collection Selection in Uncooperative Distributed Information Retrieval.- Result Merging.- Results Merging Algorithm Using Multiple Regression Models.- Segmentation of Search Engine Results for Effective Data-Fusion.- Queries.- Query Hardness Estimation Using Jensen-Shannon Divergence Among Multiple Scoring Functions.- Query Reformulation and Refinement Using NLP-Based Sentence Clustering.- Automatic Morphological Query Expansion Using Analogy-Based Machine Learning.- Advanced Structural Representations for Question Classification and Answer Re-ranking.- Relevance Feedback.- Incorporating Diversity and Density in Active Learning for Relevance Feedback.- Relevance Feedback Using Weight Propagation Compared with Information-Theoretic Query Expansion.- Evaluation.- A Retrieval Evaluation Methodology for Incomplete Relevance Assessments.- Evaluating Query-Independent Object Features for Relevancy Prediction.- Classification and Clustering.- The Utility of Information Extraction in the Classification of Books.- Combined Syntactic and Semantic Kernels for Text Classification.- Fast Large-Scale Spectral Clustering by Sequential Shrinkage Optimization.- A Probabilistic Model for Clustering Text Documents with Multiple Fields.- Filtering.- Personalized Communities in a Distributed Recommender System.- Information Recovery and Discovery in Collaborative Web Search.- Collaborative Filtering Based on Transitive Correlations Between Items.- Entropy-Based Authorship Search in Large Document Collections.- Topic Identification.- Use of Topicality and Information Measures to Improve Document Representation for Story Link Detection.- Ad Hoc Retrieval of Documents with Topical Opinion.- Expert Finding.- Probabilistic Models for Expert Finding.- Using Relevance Feedback in Expert Search.- XML IR.- Using Topic Shifts for Focussed Access to XML Repositories.- Feature- and Query-Based Table of Contents Generation for XML Documents.- Web IR.- Setting Per-field Normalisation Hyper-parameters for the Named-Page Finding Search Task.- Combining Evidence for Relevance Criteria: A Framework and Experiments in Web Retrieval.- Multimedia IR.- Classifier Fusion for SVM-Based Multimedia Semantic Indexing.- Search of Spoken Documents Retrieves Well Recognized Transcripts.- Short Papers.- Natural Language Processing for Usage Based Indexing of Web Resources.- Harnessing Trust in Social Search.- How to Compare Bilingual to Monolingual Cross-Language Information Retrieval.- Multilingual Text Classification Using Ontologies.- Using Visual-Textual Mutual Information and Entropy for Inter-modal Document Indexing.- A Study of Global Inference Algorithms in Multi-document Summarization.- Document Representation Using Global Association Distance Model.- Sentence Level Sentiment Analysis in the Presence of Conjuncts Using Linguistic Analysis.- PageRank: When Order Changes.- Model Tree Learning for Query Term Weighting in Question Answering.- Examining Repetition in User Search Behavior.- Popularity Weighted Ranking for Academic Digital Libraries.- Naming Functions for the Vector Space Model.- Effective Use of Semantic Structure in XML Retrieval.- Searching Documents Based on Relevance and Type.- Investigation of the Effectiveness of Cross-Media Indexing.- Improve Ranking by Using Image Information.- N-Step PageRank for Web Search.- Authorship Attribution Via Combination of Evidence.- Posters.- Cross-Document Entity Tracking.- Enterprise People and Skill Discovery Using Tolerant Retrieval and Visualization.- Experimental Results of the Signal Processing Approach to Distributional Clustering of Terms on Reuters-21578 Collection.- Overall Comparison at the Standard Levels of Recall of Multiple Retrieval Methods with the Friedman Test.- Building a Desktop Search Test-Bed.- Hierarchical Browsing of Video Key Frames.- Active Learning with History-Based Query Selection for Text Categorisation.- Fighting Link Spam with a Two-Stage Ranking Strategy.- Improving Naive Bayes Text Classifier Using Smoothing Methods.- Term Selection and Query Operations for Video Retrieval.- An Effective Threshold-Based Neighbor Selection in Collaborative Filtering.- Combining Multiple Sources of Evidence in XML Multimedia Documents: An Inference Network Incorporating Element Language Models.- Language Model Based Query Classification.- Integration of Text and Audio Features for Genre Classification in Music Information Retrieval.- Retrieval Method for Video Content in Different Format Based on Spatiotemporal Features.- Combination of Document Priors in Web Information Retrieval.- Enhancing Expert Search Through Query Modeling.- A Hierarchical Consensus Architecture for Robust Document Clustering.- Summarisation and Novelty: An Experimental Investigation.- A Layered Approach to Context-Dependent User Modelling.- A Bayesian Approach for Learning Document Type Relevance.

Leseproben

The Next Generation Web Search and the Demise of the Classic IR Model (p. 19)
Abstract. The classic IR model assumes a human engaged in activity that generates an "information need". This need is verbalized and then expressed as a query to search engine over a defined corpus. In the past decade, Web search engines have evolved from a first generation based on classic IR algorithms scaled to web size and thus supporting only informational queries, to a second generation supporting navigational queries using web specific information (primarily link analysis), to a third generation enabling transactional and other "semantic" queries based on a variety of technologies aimed to directly satisfy the unexpressed "user intent", thus moving further and further away from the classic model.

What is coming next? In this talk, we identify two trends, both representing "short-circuits" of the model: The first is the trend towards context driven Information Supply (IS), that is, the goal of Web IR will widen to include the supply of relevant information from multiple sources without requiring the user to make an explicit query. The information supply concept greatly precedes information retrieval, what is new in the web framework, is the ability to supply relevant information specific to a given activity and a given user, while the activity is being performed.

Thus the entire verbalization and query-formation phase are eliminated. The second trend is "social search" driven by the fact that the Web has evolved to being simultaneously a huge repository of knowledge and a vast social environment. As such, it is often more e.ective to ask the members of a given web milieu rather than construct elaborate queries. This short-circuits only the query formulation, but allows information finding activities such as opinion elicitation and discovery of social norms, that are not expressible at all as queries against a fixed corpus.

The Last Half-Century: A Perspective on Experimentation in Information Retrieval

Abstract. The experimental evaluation of information retrieval systems has a venerable history. Long before the current notion of a search engine, in fact before search by computer was even feasible, people in the library and information science community were beginning to tackle the evaluation issue. Sometimes it feels as though evaluation methodology has become fixed (stable or frozen, according to your viewpoint). However, this is far from the case. Interest in methodological questions is as great now as it ever was, and new ideas are continuing to develop. This talk will be a personal take on the field.

Learning in Hyperlinked Environments

Abstract. A remarkable number of important problems in different domains (e.g. web mining, pattern recognition, biology . . . ) are naturally modeled by functions de.ned on graphical domains, rather than on traditional vector spaces. Following the recent developments in statistical relational learning, in this talk, I introduce Diffusion Learning Machines (DLM) whose computation is very much related to Web ranking schemes based on link analysis. Using arguments from function approximation theory, I argue that, as a matter of fact, DLM can compute any conceivable ranking function on the Web.