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Improve text retrieval effectiveness...

Liu, Shuang.

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Improve text retrieval effectiveness and robustness.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Improve text retrieval effectiveness and robustness./
作者:	Liu, Shuang.
面頁冊數:	101 p.
附註:	Source: Dissertation Abstracts International, Volume: 67-09, Section: B, page: 5198.
Contained By:	Dissertation Abstracts International67-09B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3233166
ISBN:	9780542868924

Improve text retrieval effectiveness and robustness.
Liu, Shuang.

Improve text retrieval effectiveness and robustness. - 101 p.

Source: Dissertation Abstracts International, Volume: 67-09, Section: B, page: 5198.

Thesis (Ph.D.)--University of Illinois at Chicago, 2006.

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.

ISBN: 9780542868924Subjects--Topical Terms:

626642
Computer Science.

Improve text retrieval effectiveness and robustness.
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100 1 $a Liu, Shuang. $3 1669598
245 1 0 $a Improve text retrieval effectiveness and robustness.
300 $a 101 p.
500 $a Source: Dissertation Abstracts International, Volume: 67-09, Section: B, page: 5198.
500 $a Adviser: Clement Yu.
502 $a Thesis (Ph.D.)--University of Illinois at Chicago, 2006.
520 $a 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.
520 $a 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.
520 $a 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).
590 $a School code: 0799.
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773 0 $t Dissertation Abstracts International $g 67-09B.
790 1 0 $a Yu, Clement, $e advisor
790 $a 0799
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3233166