City University of Hong Kong

CityU Institutional Repository >
3_CityU Electronic Theses and Dissertations >
ETD - Dept. of Computer Science  >
CS - Doctor of Philosophy  >

Please use this identifier to cite or link to this item:

Title: Semantic pattern for question answering system
Other Titles: Ji yu yu yi mo ban de wen da xi tong yan jiu
Authors: Hao, Tianyong (郝天永)
Department: Department of Computer Science
Degree: Doctor of Philosophy
Issue Date: 2010
Publisher: City University of Hong Kong
Subjects: Question-answering systems.
Semantic computing.
Notes: CityU Call Number: QA76.9.Q4 H36 2010
96 leaves 30 cm.
Thesis (Ph.D.)--City University of Hong Kong, 2010.
Includes bibliographical references (leaves 81-96)
Type: thesis
Abstract: With the dramatic development of the Internet and the emergence of Web 2.0, User-Interactive Question Answering (UIQA) systems have been developed and become very popular Web-based services. Unlike the traditional automatic Question Answering (QA) systems which obtain answers automatically, the User-Interactive QA systems serve as interactive platforms for users to help each other with human-provided answers, which overcome the shortcoming of poor quality of the automatic answers. Surface pattern is proved an effective way to retrieve answers automatically. However, surface pattern does not include semantic information and is therefore called "poor-knowledge approaches". Hence, it cannot extract precise answers or relevant information without semantically analyzing questions and answers. To solve this problem, we firstly propose a novel type of pattern called semantic pattern and give the formal definition. The architecture of UIQA system based on semantic pattern is also presented, which includes question structure analysis, pattern matching, pattern generation, pattern classification and answer extraction. After that, to generate semantic pattern automatically and effectively, this thesis proposes a new automatic generation method of semantic patterns from free-text questions. This method uses structural processing and name entity recognition (NER) to obtain the main structure of a question. An entropy-based model is used to select suitable words from questions for generalization. WordNet is then applied in our algorithm to get the best semantic labels from our Tagger Ontology for such chosen words. An evaluation method is also proposed to estimate the suitability of the generated patterns and is implemented in a real UIQA system. Experiments with 5500 questions show that 63.9% generated patterns are satisfactory on average. Finally, this thesis presents one of the applications of semantic pattern as an example - an automatic method for building a semantic dictionary from existing semantic pattern based questions for question categorization. This dictionary consists of two main parts: Semantic Domain Terms (SDT), which is a domain specific term list, and Semantic Labeled Terms (SLT), which contains common terms tagged with semantic labels. We implement the semantic dictionary construction method on a set of 2509 questions with semantic patterns in our system. Experimental results show that the precision of question classification is improved by 7.5% on average after using the constructed semantic dictionary compared with the baseline method.
Online Catalog Link:
Appears in Collections:CS - Doctor of Philosophy

Files in This Item:

File Description SizeFormat
abstract.html132 BHTMLView/Open
fulltext.html132 BHTMLView/Open

Items in CityU IR are protected by copyright, with all rights reserved, unless otherwise indicated.


Valid XHTML 1.0!
DSpace Software © 2013 CityU Library - Send feedback to Library Systems
Privacy Policy · Copyright · Disclaimer