Applying genetic algorithms to query optimization in document retrieval

This paper proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. One of the contributions of the paper is to combine the Bigram (Chen, A., He, J., Xu, L., Gey, F. C., & Meggs, J. 1997. Chinese text retrieval without using a di...

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Published in:	Information processing & management Vol. 36; no. 5; pp. 737 - 759
Main Authors:	Horng, Jorng-Tzong, Yeh, Ching-Chang
Format:	Journal Article
Language:	English
Published:	Oxford Elsevier Ltd 01.09.2000 Elsevier Science Elsevier Science Ltd
Subjects:	Algorithms Bigram Strategy Chinese Content analysis Exact sciences and technology Genetic algorithms Genetics Indexing. Classification. Abstracting. Syntheses Information and communication sciences Information and document structure and analysis Information processing and retrieval Information Retrieval Information science. Documentation Keyword Keywords Mathematical Formulas Online information retrieval Optimization PAT-tree Query Processing Relevance (Information Retrieval) Retrieval Sciences and techniques of general use Searching Studies Vector Spaces Weighted Term Searching Weighting Keyword Information retrieval PAT-tree Performance evaluation Automatic classification Weighting Genetic algorithm Automatic indexing Feedback regulation Cluster Method Comparative study Optimization
ISSN:	0306-4573, 1873-5371
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Abstract	This paper proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. One of the contributions of the paper is to combine the Bigram (Chen, A., He, J., Xu, L., Gey, F. C., & Meggs, J. 1997. Chinese text retrieval without using a dictionary, ACM SIGIR’97, Philadelphia, PA, USA, pp. 42–49; Yang, Y.-Y., Chang, J.-S., & Chen, K.-J. 1993), Document automatic classification and ranking, Master thesis, Department of Computer Science, National Tsing Hua University) model and PAT-tree structure (Chien, L.-F., Huang, T.-I., & Chien, M.-C. 1997 Pat-tree-based keyword extraction for Chinese information retrieval, ACM SIGIR’97, Philadelphia, PA, US, pp. 50–59) to retrieve keywords. The approach extracts bigrams from documents and uses the bigrams to construct a PAT-tree to retrieve keywords. The proposed approach can retrieve any type of keywords such as technical keywords and a person’s name. Effectiveness of the proposed approach is demonstrated by comparing how effective are the keywords found by both this approach and the PAT-tree based approach. This comparison reveals that our keyword retrieval approach is as accurate as the PAT-tree based approach, yet our approach is faster and uses less memory. The study then applies genetic algorithms to tune the weight of retrieved keywords. Moreover, several documents obtained from web sites are tested and experimental results are compared with those of other approaches, indicating that the proposed approach is highly promising for applications.
AbstractList	Proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. Discusses Chinese text retrieval, term frequency rating formulas, vector space models, bigrams, the PAT-tree structure for information retrieval, query vectors, and relevance feedback. (Author/LRW) This paper proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. One of the contributions of the paper is to combine the Bigram (Chen, A., He, J., Xu, L., Gey, F. C., & Meggs, J. 1997. Chinese text retrieval without using a dictionary, ACM SIGIR’97, Philadelphia, PA, USA, pp. 42–49; Yang, Y.-Y., Chang, J.-S., & Chen, K.-J. 1993), Document automatic classification and ranking, Master thesis, Department of Computer Science, National Tsing Hua University) model and PAT-tree structure (Chien, L.-F., Huang, T.-I., & Chien, M.-C. 1997 Pat-tree-based keyword extraction for Chinese information retrieval, ACM SIGIR’97, Philadelphia, PA, US, pp. 50–59) to retrieve keywords. The approach extracts bigrams from documents and uses the bigrams to construct a PAT-tree to retrieve keywords. The proposed approach can retrieve any type of keywords such as technical keywords and a person’s name. Effectiveness of the proposed approach is demonstrated by comparing how effective are the keywords found by both this approach and the PAT-tree based approach. This comparison reveals that our keyword retrieval approach is as accurate as the PAT-tree based approach, yet our approach is faster and uses less memory. The study then applies genetic algorithms to tune the weight of retrieved keywords. Moreover, several documents obtained from web sites are tested and experimental results are compared with those of other approaches, indicating that the proposed approach is highly promising for applications. This paper proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. Proposes a novel approach to automatically retrieve keywords and uses genetic algorithms to adapt the keyword weights. Combines the bigram model and PAT-tree structure to retrieve keywords. The approach extracts bigrams from documents and uses the bigrams to construct a PAT-tree to retrieve keywords. The proposed approach can retrieve any type of keywords such as technical keywords and a person's name. Effectiveness of the proposed approach is demonstrated by comparing how effective are the keywords found by both this approach and the PAT-tree based approach, but this approach is faster and uses less memory. Applies genetic algorithms to tune the weight of retrieved keywords. Several documents obtained from web sites are tested and experimental results are compared with those of other approaches, indicating that the proposed approach is promising for applications. (Original abstract - amended)
Author	Yeh, Ching-Chang Horng, Jorng-Tzong
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Snippet	This paper proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. One of the contributions... Proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. Discusses Chinese text retrieval,... This paper proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. Proposes a novel approach to automatically retrieve keywords and uses genetic algorithms to adapt the keyword weights. Combines the bigram model and PAT-tree...
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SubjectTerms	Algorithms Bigram Strategy Chinese Content analysis Exact sciences and technology Genetic algorithms Genetics Indexing. Classification. Abstracting. Syntheses Information and communication sciences Information and document structure and analysis Information processing and retrieval Information Retrieval Information science. Documentation Keyword Keywords Mathematical Formulas Online information retrieval Optimization PAT-tree Query Processing Relevance (Information Retrieval) Retrieval Sciences and techniques of general use Searching Studies Vector Spaces Weighted Term Searching Weighting
Title	Applying genetic algorithms to query optimization in document retrieval
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