Abstract/Details

Searching question and answer archives


2007 2007

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Abstract (summary)

Archives of questions and answers are a valuable information source. However, little research has been done to exploit them. We propose a new type of information retrieval system that answers users' questions by searching question and answer archives. The proposed system has many advantages over current web search engines. In this system, natural language questions are used instead of keyword queries, and the system directly returns answers instead of lists of documents. Two most important challenges in the implementation of the system are finding semantically similar questions to the user question and estimating the quality of answers. We propose using a translation-based retrieval model to overcome the word mismatch problem between questions. Our model combines the advantages of the IBM machine translation model and the query likelihood language model and shows significantly improved retrieval performance over the state of the art retrieval models. We also show that collections of question and answer pairs are good linguistic resources for learning reliable word-to-word translation relationships. To avoid returning bad answers to users, we build an answer quality predictor based on statistical machine learning techniques. By combining the quality predictor with the translation-based retrieval model, our system successfully returns relevant and high quality answers to the user.

Indexing (details)


Subject
Computer science
Classification
0984: Computer science
Identifier / keyword
Applied sciences, Question and answer archives, Searching, Word mismatch
Title
Searching question and answer archives
Author
Jeon, Jiwoon
Number of pages
147
Publication year
2007
Degree date
2007
School code
0118
Source
DAI-B 68/11, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780549330660
Advisor
Croft, W. Bruce
Committee member
Allan, James; Diao, Yanlei; Kim, Byoung
University/institution
University of Massachusetts Amherst
Department
Computer Science
University location
United States -- Massachusetts
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3289257
ProQuest document ID
304845715
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/304845715
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