Query Expansion for Answer Document Retrieval in Chinese Question Answering System

Yu, Zhengtao; Zheng, Zedong; Tang, Shiping; Guo, Jianyi

doi:10.1109/icmlc.2005.1526922

Cited by 3 publications

(6 citation statements)

References 3 publications

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“…This method can also refine the related words compare to the method in Ref. [9]. For example, the question "泰山的高度是多少？" (How high is the Taishan?)…”

Section: Experiments and Evaluationmentioning

confidence: 95%

“…For Chinese question answering system, correct answers are needed to be further extracted from the recalled documents, so the evaluation emphasizes on whether or not the recalled documents contain correct answer documents. Therefore, there is a comparatively simple method for evaluating precision [9] . Here a presents a question, n presents the top-n documents.…”

Section: Experiments and Evaluationmentioning

confidence: 99%

“…The experiment uses precision P a@n to evaluate answer document retrieval. We compare the retrieval performance of our query expansion method with the baseline method (without query expansion), the feedback method [5] , and the question type method [9] . We submit the query words and the expanded query words to Baidu search engine.…”

Section: Experiments and Evaluationmentioning

confidence: 99%

“…The second method is based on a certain semantic knowledge resource [6] , which expands query words directly according to their semantic relation (for example, synonymy), such as"WordNet" [7] and "HowNet" [8] or other thesaurus. Thirdly, Yu et al [9] propose a query expansion method which expands query according to the question types parsed by questions and the related words for specific question types obtained by statistical analysis.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Query expansion based on semantics and statistics in Chinese question answering system

Jia¹,

Pang

Li³

et al. 2008

Wuhan Univ. J. Nat. Sci.

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In Chinese question answering system, because there is more semantic relation in questions than that in query words, the precision can be improved by expanding query while using natural language questions to retrieve documents. This paper proposes a new approach to query expansion based on semantics and statistics. Firstly automatic relevance feedback method is used to generate a candidate expansion word set. Then the expanded query words are selected from the set based on the semantic similarity and semantic relevancy between the candidate words and the original words. Experiments show the new approach is effective for Web retrieval and out-performs the conventional expansion approaches.A typical question answering system is made up of four modules which are question analysis, document retrieval, candidate answer extraction and answer selection. When using question presented with natural language to retrieve document, query words extraction and query expansion are the key factors that affect its retrieval performance. After stop-words are removed from Chinese question segmentation, nouns, adverbs and adjectives are extracted to form the query. The query is submitted to search engine to retrieve relevant documents. The object of information retrieval is to provide the user with a set of documents that are relevant to the query, at the same time to eliminate the irrelevant ones from the retrieved set. Currently, a fundamental problem in document retrieval is term mismatch between queries and documents [1] .With the explosive growth of information in Internet, the word mismatch problem in a more general context becomes more significant as observed by Furnas [2] . In his experiments, the probability of two users using the same term to describe an object is less than 20%. Addressing the word mismatch problem has become an increasingly important research topic in IR. For query already generated, it is necessary to expand query. A query is expanded by adding other terms closely related to the original query terms. In the past years, there are three kinds of query expansion methods in information retrieval. The first is automatic relevant feedback [3][4][5] , which statistically analyzes the initial retrieval docu-

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“…This method can also refine the related words compare to the method in Ref. [9]. For example, the question "泰山的高度是多少？" (How high is the Taishan?)…”

Section: Experiments and Evaluationmentioning

confidence: 95%

Section: Experiments and Evaluationmentioning

confidence: 99%

Section: Experiments and Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Query expansion based on semantics and statistics in Chinese question answering system

Jia¹,

Pang

Li³

et al. 2008

Wuhan Univ. J. Nat. Sci.

View full text Add to dashboard Cite

show abstract

“…The second method is based on a certain semantic knowledge 978-1-4244-2780-2/08/$25.00 ©2008 IEEE resource [3], which expands query words directly according to the semantic relation of query words (for example: synonymy), such as "WordNet" [4] and "HowNet" [5] or other thesaurus. Thirdly, Yu [6] etc proposes a query expansion method which expands query according to question types parsed by questions and related words for specific question types obtained by statistical analysis.…”

Section: Introductionmentioning

confidence: 99%

Answer extraction based on query expansion in Chinese question answering system

Jia

Sun

2008

2008 International Conference on Natural Language Processing and Knowledge Engineering

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When using natural language question to retrieval document, query expansion are the key factors that affect its retrieval performance. By analyzing the traditional query expansion method, this paper puts forward a query expansion method based on set theory for answering document retrieval. In order to verify the validity of the method, a similarity calculation method for question and candidate answer sentences is proposed to extract the answer. The major query expansion terms and the minor query expansion terms are taken into consideration with different weight in the similarity calculation. The experiment results show that the performance makes substantial improvement.

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Research and Design on Personalized DL Based on J2EE

Kang

Chen

2008

2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application

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From the angle of natural language processing, this paper analyses the architecture and core technologies of personalized digital library. It introduces matching strategy of document content representation and topic search. This matching strategy is a expansion model of Vector Space Model. It uses Word Relating Matrix base on tradition VSM. The WRM is computed by cooccurrence and mutual information of index word. Experiment data show the strategy used WRM is better than tradition VSM. At the same time personalized digital library is designed based on J2EE according to above strategy. It provides a project for personalized DL searching information in isomer database. This paper also introduces the key technologies based on J2EE, which includes EJB delegate technology, joint common pool technology and so on.

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Query Expansion for Answer Document Retrieval in Chinese Question Answering System

Cited by 3 publications

References 3 publications

Query expansion based on semantics and statistics in Chinese question answering system

Query expansion based on semantics and statistics in Chinese question answering system

Answer extraction based on query expansion in Chinese question answering system

Research and Design on Personalized DL Based on J2EE

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