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Semantic Document Retrieval for English to Chinese Cross-Language Question Answering System

Author: YangTian
Tutor: HuangDeGen
School: Dalian University of Technology
Course: Applied Computer Technology
Keywords: Cross-language question answering system Cross-language information retrieval Query Expansion Semantic topic clustering
CLC: TP391.1
Type: Master's thesis
Year: 2011
Downloads: 21
Quote: 0
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Abstract


With the development of modern Internet technologies, the information resources of the Internet, more and more Internet users has increased year by year, the number of languages ??describe information resources increased gradually. Information users need to find such large-scale, complex information environment has become a very important research topics. Cross-language information retrieval has gradually become the focus of attention of researchers. An advanced form of cross-language information retrieval system, cross-language question answering system has also become a popular research directions in the field of natural language processing. Compared to traditional information retrieval systems, the problem of cross-language question answering system queries for a complete and colloquial, the result returned is the high accuracy of the web page, or a clear answer. From within the system, cross-language question answering system using a large number of natural language processing technologies, such as natural language parsing, problem analysis, named entity recognition and machine translation. Cross-language question answering system can be divided into two parts, cross-language document retrieval and answer extraction. The main functions of the cross-language document retrieval document may contain answers retrieved by analysis of the source language query in the target language. This paper is focused on this part of the study. The lack of cross-language document retrieval method by analyzing the existing cross-language questions and answers, this paper presents a semantic query expansion and semantic topic clustering-based information retrieval method, designed to get more and more relevant documents in the target language document. First of all, the type of analysis of the source language (English) queries and extract keywords, key words translated into the target language (Chinese) query words, and combined into a query based on certain criteria; Secondly, the use of local semantic analysis of the original query. expansion and information retrieval system to retrieve the query target language document; Finally, the document has been generated based on semantic topic clustering method to reorder the relevant document containing the query answer. The main contribution of this paper includes the following aspects: (1) in the query expansion process, the effective use of semantic information between the original query keywords expansion words. Network resources to search out the slice (snippets) as an extension of the basis of the document collection. The same time, through the use of extended local semantic analysis-based approach key words, to solve the problem of lack of information of the original query. (2) design a reordering of the initial search results based on the the semantic topic clustering results sorting method. Solve problem related documentation Sort rely. (3) Avoid the theme offset the resulting document.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Text Processing
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