Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Approaches to passage retrieval in full text information systems
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Passage-based query refinement (MultiText experiments for TREC-6)
Information Processing and Management: an International Journal - The sixth text REtrieval conference (TREC-6)
Natural Language Information Retrieval
Natural Language Information Retrieval
Modern Information Retrieval
Text Segmentation into Paragraphs Based on Local Text Cohesion
TSD '01 Proceedings of the 4th International Conference on Text, Speech and Dialogue
QA on the Web: A Preliminary Study for Spanish Language
ENC '04 Proceedings of the Fifth Mexican International Conference in Computer Science
Passage selection to improve Question Answering
MultiSumQA '02 proceedings of the 2002 conference on multilingual summarization and question answering - Volume 19
Using measures of semantic relatedness for word sense disambiguation
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
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Automatically recognizing in large electronic texts short selfcontained passages relevant for a user query is necessary for fast and accurate information access to large text archives. Surprisingly, most search engines practically do not provide any help to the user in this tedious task, just presenting a list of whole documents supposedly containing the requested information. We show how different sources of evidence can be combined in order to assess the quality of different passages in a document and present the highest ranked ones to the user. Specifically, we take into account the relevance of a passage to the user query, structural integrity of the passage with respect to paragraphs and sections of the document, and topic integrity with respect to topic changes and topic threads in the text. Our experiments show that the results are promising.