Difference between revisions of "Improve text retrieval effectiveness and robustness"

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Revision as of 18:40, December 3, 2013

Publication (help)
Improve text retrieval effectiveness and robustness
Authors: Shuang Liu [edit item]
Citation: University of Illinois at Chicago  : . 2006. United States, Illinois.
Publication type: Thesis
Peer-reviewed: Yes
Database(s):
DOI: Define doi.
Google Scholar cites: Not available
Link(s): Paper link
Added by Wikilit team: Added on initial load
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Improve text retrieval effectiveness and robustness is a publication by Shuang Liu.


[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).

[edit] Research questions

"This study focuses on the following: 1. designing and implementing a new retrieval model 2. utilizing concept in text retrieval 3. designing and implementing a highly accurate 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 pseuda relevance feedback into the retrieval system including local feedback, web-assisted feedback, and wikipedia assisted feedback"

Research details

Topics: Textual information retrieval [edit item]
Domains: Computer science [edit item]
Theory type: Design and action [edit item]
Wikipedia coverage: Sample data [edit item]
Theories: "Undetermined" [edit item]
Research design: Experiment [edit item]
Data source: [edit item]
Collected data time dimension: Cross-sectional [edit item]
Unit of analysis: Article [edit item]
Wikipedia data extraction: Live Wikipedia [edit item]
Wikipedia page type: Article [edit item]
Wikipedia language: English [edit item]

[edit] Conclusion

"The experimental results showed an increase in the performance of the retrieval system after using the techniques proposed"

[edit] Comments


Further notes[edit]

Facts about "Improve text retrieval effectiveness and robustness"RDF feed
AbstractRetrieval effectiveness and robustness areRetrieval 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).d geometric mean average precision (GMAP).
Added by wikilit teamAdded on initial load +
Collected data time dimensionCross-sectional +
ConclusionThe experimental results showed an increase in the performance of the retrieval system after using the techniques proposed
Conference locationUnited States, Illinois +
Google scholar urlhttp://scholar.google.com/scholar?ie=UTF-8&q=%22Improve%2Btext%2Bretrieval%2Beffectiveness%2Band%2Brobustness%22 +
Has authorShuang Liu +
Has domainComputer science +
Has topicTextual information retrieval +
Peer reviewedYes +
Publication typeThesis +
Published inUniversity of Illinois at Chicago +
Research designExperiment +
Research questionsThis study focuses on the following:

1. deThis study focuses on the following: 1. designing and implementing a new retrieval model 2. utilizing concept in text retrieval 3. designing and implementing a highly accurate 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 pseuda relevance feedback into the retrieval system including local feedback, web-assisted feedback, and wikipedia assisted feedback feedback, and wikipedia assisted feedback
Revid10,166 +
TheoriesUndetermined
Theory typeDesign and action +
TitleImprove text retrieval effectiveness and robustness
Unit of analysisArticle +
Urlhttp://proquest.umi.com/pqdweb?did=1221734581&Fmt=7&clientId=10306&RQT=309&VName=PQD +
Wikipedia coverageSample data +
Wikipedia data extractionLive Wikipedia +
Wikipedia languageEnglish +
Wikipedia page typeArticle +
Year2006 +