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研究生： 洪琴婷
Chin-Ting Hung
論文名稱： LSI-based Document Retrieval
LSI-based Document Retrieval
指導教授： 邱貴發
Chiou, Guey-Fa
學位類別： 碩士
Master
系所名稱： 資訊教育研究所
Graduate Institute of Information and Computer Education
論文出版年： 2002
畢業學年度： 90
語文別： 英文
論文頁數： 105
中文關鍵詞： latent semantic indexing 、information retrieval 、singular value decomposition 、relevance feedback
英文關鍵詞： latent semantic indexing, information retrieval, singular value decomposition, relevance feedback
論文種類： 學術論文
相關次數： 點閱：293  下載：12 
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    • Latent Semantic Indexing (LSI) is a retrieval technique that employs Singular Value Decomposition (SVD) and maps each document vector into a lower dimensional space to achieve concept matching. LSI has been proved that it has a better performance than traditional lexical searching methods and has the ability to overcome synonym and polysemy problems. Our purposes were to construct an LSI model to facilitate the retrieving process, and to propose potential uses of LSI in education.
    We used five test collections, two Chinese and three English to verify our LSI model. The standard test collection, MED, was used to verify the correctness of our system, and the collections of ERIC and English educational abstracts were used to test the feasibility of LSI in educational materials; in addition, two Chinese test collections were used to examine the LSI usability on Chinese documents. Our major concerns in the tests were term weighting, stemming, reduction dimensions, and relevance feedback.
    Results showed that the LSI system model worked well not only for English documents but also for character-based Chinese documents. The LSI method could effectively group semantically relevant documents. The better weighting types were log idf, log entropy, log gfidf, tf idf, and tf gfidf. Results also indicated significant improvement in retrieval after stemming. Relevance feedback with different weighting ratio worked well. And the best dimension value in ERIC documents was around 50 or 60. In conclusion, we believed that LSI is a suitable system model for retrieving relevant documents.
    Keywords: latent semantic indexing (LSI), information retrieval (IR), singular value decomposition (SVD), relevance feedback.

    Latent Semantic Indexing (LSI) is a retrieval technique that employs Singular Value Decomposition (SVD) and maps each document vector into a lower dimensional space to achieve concept matching. LSI has been proved that it has a better performance than traditional lexical searching methods and has the ability to overcome synonym and polysemy problems. Our purposes were to construct an LSI model to facilitate the retrieving process, and to propose potential uses of LSI in education.
    We used five test collections, two Chinese and three English to verify our LSI model. The standard test collection, MED, was used to verify the correctness of our system, and the collections of ERIC and English educational abstracts were used to test the feasibility of LSI in educational materials; in addition, two Chinese test collections were used to examine the LSI usability on Chinese documents. Our major concerns in the tests were term weighting, stemming, reduction dimensions, and relevance feedback.
    Results showed that the LSI system model worked well not only for English documents but also for character-based Chinese documents. The LSI method could effectively group semantically relevant documents. The better weighting types were log idf, log entropy, log gfidf, tf idf, and tf gfidf. Results also indicated significant improvement in retrieval after stemming. Relevance feedback with different weighting ratio worked well. And the best dimension value in ERIC documents was around 50 or 60. In conclusion, we believed that LSI is a suitable system model for retrieving relevant documents.
    Keywords: latent semantic indexing (LSI), information retrieval (IR), singular value decomposition (SVD), relevance feedback.

    Contents 1. Introduction…………………………………………………………1 1.1 Problem Statement…………………………………………………1 1.2 Goals…………………………………………………………………1 1.3 Overview of Latent Semantic Indexing………………………2 2. Related work…………………………………………………………4 2.1 Retrieval Techniques……………………………………………4 2.1.1 Information Retrieval and Information Filtering………4 2.1.2 Lexical Pattern Matching Model……………………………6 2.1.3 Boolean Model…………………………………………………6 2.1.4 Probabilistic Model……………………………………………8 2.1.5 Basic Vector Model……………………………………………11 2.2 Vector Space Model………………………………………………15 2.3 Latent Semantic Indexing………………………………………22 2.3.1 The Fundamental Idea…………………………………………22 2.3.2 Advantages of Latent Semantic Indexing…………………23 2.3.3 Representation of Term-Document Matrix…………………24 2.3.4 Singular Value Decomposition………………………………28 2.3.5 Query Projection………………………………………………33 2.3.6 Updating…………………………………………………………34 2.3.7 Downdating………………………………………………………38 2.3.8 Relevance Feedback……………………………………………39 2.4 Potential Uses of LSI for Teaching and Learning………40 2.4.1 Optimal Text for Learning…………………………………41 2.4.2 Coherence and Comprehensibility Measurement…………41 2.4.3 Connecting Students with Each Other and with Relevant Experts…………………………………………………………41 2.4.4 Recommendation System………………………………………42 2.4.5 Cross Language Retrieval…………………………………43 2.4.6 Automatic Writing Assessment………………………………43 2.4.7 Constructing a Summary………………………………………44 2.4.8 Portfolio Assessment…………………………………………45 2.5 LSI-based IR in Chinese Language……………………………45 2.6 Evaluation of LSI-based IR……………………………………47 3. The System…………………………………………………………50 3.1 System Environments……………………………………………50 3.2 System Architecture and Processes…………………………50 3.2.1 Basic Scheme of the System………………………………50 3.2.2 The System Architecture of Retrieving Information…51 3.2.3 Indexing and Clustering Processes………………………52 3.3 System Components………………………………………………68 4. Results and Discussion…………………………………………71 4.1 Test Collections………………………………………………71 4.2 Performance Evaluation in English Collections…………74 4.2.1 Different Weighting Types for the original MED document………………………………………74 4.2.2 Different Weighting Types for the stemmed MED document…75 4.2.3 Comparing original and stemmed MED in Different Weighting Types………………………………76 4.2.4 Different Weighting Types for the original ERIC document…………………………………79 4.2.5 Different Weighting Types for the stemmed ERIC document…………………………………80 4.2.6 Comparing original and stemmed ERIC in Different Weighting Types………………………………81 4.2.7 Reduction of Dimensions in ERIC document………………84 4.2.8 English Educational Abstracts……………………………88 4.2.9 Original and Stemmed English Educational Abstracts…90 4.3 Performance Evaluation in Chinese Collections…………91 4.3.1 Different Weighting Types for the Chinese Document about Health…………………………………………………91 4.3.2 Different Weighting Types for the Chinese Document about environment education……………………………92 4.4 Experiments on Relevance Feedback………………………93 5. Conclusions and Future Studies………………………………95 5.1 Conclusions………………………………………………………95 5.2 Future Studies…………………………………………………96 References………………………………………………………………97 Appendix A: Samples of Test Collections………………………100

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