Improve text retrieval effectiveness and robustness

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Improve text retrieval effectiveness and robustness is a 2006 doctoral thesis written in English by Shuang Liu and published in University of Illinois at Chicago.

[edit] Abstract

Retrieval effectiveness and robustness are two of the most important criteria of text retrieval. Over the past decades, numerous techniques have been introduced to enhance text retrieval performance including those using phrases, passages, general dictionaries such as {WordNet,} word sense disambiguation, automatic query expansion, pseudo-relevance feedback, and external sources assisted feedback. This {Ph.D.} dissertation study focuses on improving the text retrieval effectiveness and robustness by extending existing retrieval model and providing new techniques which include: {(1)?Designing} and implementing a new retrieval model. {(2)?Utilizing} concept in text retrieval. {(3)?Designing} and implementing a highly accurate word sense disambiguation algorithm and incorporating it to our information retrieval system. {(4)?Expanding} queries by using multiple dictionaries such as {WordNet} and Wikipedia. {(5)?Employing} different pseudo relevance feedback into the retrieval system including local, web-assisted, and Wikipedia-assisted feedback and adopting semantic information to pseudo relevance feedback. In this {Ph.D.} study, our design decisions are verified through experiments in the retrieval system. Results are evaluated by standard evaluation metrics: precision, recall, mean average precision (MAP), and geometric mean average precision (GMAP)

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