Creating multi-level class hierarchy for question classification with NP analysis and wordnet

作     者：Bakhtyar, Maheen Kawtrakul, Asanee Baber, Junaid Doudpota, Sher Muhammad 

作者机构：Department of Computer Science and Information Management Asian Institute of Technology Thailand Department of Computer Science and IT University of Balochistan Pakistan Department of Computer Engineering Kasetsart University Thailand National Electronics and Computer Technology Center Thailand Department of Computer Science Sukkur Institute of Business Administration Pakistan 

出 版 物：《Journal of Digital Information Management》 (J. Digit. Inf. Manage.)

年 卷 期：2012年第10卷第6期

页      面：379-388页

核心收录：

学科分类：1205[管理学-图书情报与档案管理] 0810[工学-信息与通信工程] 12[管理学] 120501[管理学-图书馆学] 0835[工学-软件工程] 120502[管理学-情报学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Search engines 

摘      要：Question answering systems provide answer to the questions using various question processing techniques and extracts answers from a set of documents. Finding the exact answer is more interesting and useful than getting a list of documents to look through and find the answer manually. Question answering is not same as a traditional document retrieval search engine where a set of relevant documents are returned in response of the query, whereas, in the question answering systems the response of the query is a concise and exact answer to the question. Typically, Question Classification (QC) is the first step in a Question Answering (QA) system. This phase is responsible for finding out the type of the expected answer by pruning out the extra information that is not relevant to extract the answer. Almost all the previous QC algorithms evaluated their work on the basis of a common class hierarchy already defined. The coarse grained classes Location, Entity and Numeric in the existing hierarchy have a fine grained class Other. We present the framework to create new fine grained classes to replace the Other classes. We also discuss the motivation behind the replacement and how the new fine grained classes may support the answer extraction. Additionally, we also present an automatic hierarchy creation method to add new class nodes using WordNet and Noun phrase parsing.