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Class-free answer typing Open Access


Other title
Question Answering
Answer Typing
Artificial Intelligence
Natural Language Processing
Type of item
Degree grantor
University of Alberta
Author or creator
Pinchak, Christopher
Supervisor and department
Lin, Dekang (Computing Science)
Rafiei, Davood (Computing Science)
Examining committee member and department
Shiri, Ali (Library and Information Studies)
Lin, Dekang (Computing Science)
Nascimento, Mario (Computing Science)
Kondrak, Grzegorz (Computing Science)
Srihari, Rohini (Computer Science, State University of New York at Buffalo)
Rafiei, Davood (Computing Science)
Department of Computing Science

Date accepted
Graduation date
Doctor of Philosophy
Degree level
Answer typing is an important aspect of the question answering process. Most commonly addressed with the use of a fixed set of possible answer classes via question classification, answer typing influences which answers will ultimately be selected as correct. Answer typing introduces the concept of type-appropriate responses. Such responses are plausible in the context of question answering when they are believable as answers to a given question. This notion of type-appropriateness is distinct from correctness, as there may exist many type-appropriate responses that are not correct answers. Type-appropriate responses can even exist for other kinds of queries that are not strictly questions. This work introduces class-free models of answer type for certain kinds of questions as well as models of type-appropriateness useful to the domain of information retrieval. Models built for both open-ended noun phrase questions and how-adjective questions are designed to evaluate the type-appropriateness of a candidate answer directly rather than via the use of an intermediary question class (as is done with question classification). Experiments show a meaningful improvement over alternative typing strategies for these kinds of questions. Ideas from these models are then applied outside of the domain of question answering in an effort to improve traditional information retrieval results. Experiments comparing reranked results with those of the Google search engine show improvements are made in those rare situations for which Google provides less than ideal results.
License granted by Christopher Pinchak ( on 2009-06-04T18:10:47Z (GMT): Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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