博碩士論文 89522063 詳細資訊




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姓名 吳東軒(Dong-shun Wu)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 中文資料擷取系統之設計與研究
(Mining Relevant Syntactic Patterns for Chinese Text Extraction)
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摘要(中) 在資訊化時代的今日,大量的資料正在慢慢的電子化中,再加上網際網路的蓬勃發展,新的資訊正每天不斷地在網路的脈絡中流動及累積,面對隨著時間不斷地增加的訊息,要從中尋得個人所需的資訊是相當困難的。以電子化的優點,從龐大的資料中,利用電腦快速且精準地找到我們所需的資訊,這正是資訊擷取(Information Extraction)的精神所在。
資訊擷取系統在英文的處理方面,已經發展有一段時間了,但是對於中文的處理方面,仍然有很大的發展空間。由於中文文法中,句型結構相對於英文來說是較為鬆散的,因此中文資訊擷取系統很難利用英文資訊擷取系統中常使用的句型分析來幫助資訊的擷取。在本篇論文中,我們針對中文的純文字資料的擷取問題,提出了一套流程,希望透過這一流程,順利地從中文純文字資料中,擷取出我們所需的資訊。
摘要(英) IE is a research topic related to TREC (Text Retrieval Conference) and MUC (Message Understanding Conference). The target of Information extraction (IE) is to extract specific types of information from text. The IE systems for free text form written in English are different from the systems for Chinese.
In this paper we propose a simple method for extracting information from free text from written in Chinese. We use training examples and encode them with the responding targets. Then we find the repeated substrings within the encoded text. These repeated substrings play the role in our IE system for Chinese which is likes the role of the sentence analyzers in some IE systems for free text form in English. In the phrase for extracting information from testing data, we first encode them and then extract the interesting target by the repeated substrings fined previously.
關鍵字(中) ★ 資訊擷取系統 關鍵字(英) ★ iformation extraction
論文目次 第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 1
1.3 論文結構 2
第二章 相關研究 3
2.1 AutoSlog及AutoSlog-TS系統 4
2.1.1 AutoSlog系統 4
2.1.2 AutoSlog-TS系統 7
2.2 RAPIER系統 9
2.3 DiscoTEX系統 13
第三章 系統設計及演算法 17
3.1 資料的前置處理 18
3.2 訓練階段 19
3.2.1 學習範例的編碼 20
3.2.2 挑選重要辭彙片語 21
3.3 測試階段 23
3.3.1 測試資料的編碼 23
3.3.2 重要辭彙片語與字串區域性調整對齊 24
3.3.3 演算法 25
第四章 實驗與討論 27
4.1 資料描述 27
4.2 學習效果評量 29
4.3 測試效果評量 30
4.4 討論 33
第五章 應用 36
5.1 查詢流程的設計 36
第六章 結論 39
參考文獻 40
參考文獻 [1] H. Ahonen, O. Heinonen, M. Klemettinen, and A. I. Verkamo. Applying data mining techniques for descriptive phrases extraction in digital document collections. In Advances in Digital Libraries (ADL’98), Santa Barbara, California, USA, 1998. IEEE Computer Society Press.
[2] H. Ahonen. Finding all maximal frequent sequences in text. In ICML99 Workshop, Machine Learning in Text Data Analysis, Bled, Slovenia, 1999.
[3] H. Ahonen. Knowledge discovery in documents by extracting frequent word sequences. Library Trends, 1999.
[4] H. Ahonen, O. Heinonen, M. Klemettinen, and A. I. Verkamo. Finding co-occurring Text Phrases by combining sequence and frequent set discovery. IJCAI-1999.
[5] M.E. Califf, and R.J. Mooney. Relational learning of pattern-match rules for information extraction. In Proceedings of the 16th National Conference on AI, 328-334, 1999.
[6] M.E. Califf and R.J. Mooney. Workshop on Frontiers of Inductive Logic Programming. IJCAI-97, pp. 7-11.
[7] L.F. Chien, PAT-Tree Based keyword extraction for Chinese Information Retrieval, SIGIR-1997.
[8] William W. Cohen, Fast Effective Rule Induction. In Proceedings of the Twelfth International Conference on Machine Learning (ICML-95), p.115-123.
[9] R. Feldman and I. Dagan. Knowledge discovery in textual databases (KDT). KDD-1995.
[10] R. Feldman, I. Dagan, and W. Klosgen. Efficient algorithms for mining and manipulating associations in texts. In Cybernetics and Systems, Vol. II, The 13th European Meeting on Cybernetics and Systems Research, Vienna, Austria, Apr. 1996.
[11] B. Glasgow, et al. MITA: An information extraction approach to analysis of free-form text in life insurance applications
[12] Huffman, S. Learning information extraction patterns from examples. IJCAI-95 Workshop on new approaches to learning for natural language processing, p.127-142.
[13] Kim, J. and Moldovan, D. Acquisition of linguistic patterns for Knowledge-based information extraction. IEEE Transactions on Knowledge and Data Engineering 7(5):713-724.
[14] Krupka, G. Description of the SRA system as used for MUC-6. Proceedings of the Sixth Message Understanding Conference (MUC-6), p.221-235.
[15] Lehnert, W. 1990. Symbolic/Subsymbolic Sentence Analysis: Exploiting the Best of Two Worlds. In Barnden, J., and Pollack, J., editors 1990, Advances in Connectionist and Neural Computation Theory, Vol. 1. Ablex Publishers, Norwood, NJ. P.135-164.
[16] B. Lent, R. Agrawal, and R. Srikant. Discovering trends in text databases. KDD-1997, pp. 226-230. AAAI Press.
[17] U. Y. Nahm and R. J. Mooney. A mutually beneficial integration of data mining and information extraction. AAAI-2000, pp. 627-632, Austin, TX.
[18] Un Yong Nahm and Raymond J. Mooney. Using information extraction to aid the discovery of prediction rules from texts. KDD-2000, pp. 51-58.
[19] Un Yong Nahm and Raymond J. Mooney. A mutually beneficial integration of data mining and information extraction. National Conference on Artificial Intelligence 2000, pages 627-632.
[20] Un Yong Nahm and Raymond J. Mooney. Text Mining and Information Extrction. Submitted to the AAAI 2002.
[21] M. Rajman and R. Besancon. Text Mining – Knowledge extraction from unstructured textual data. IFCS-1998.
[22] Ellen Riloff. Automatically Constructing a Dictionary for Information Extraction Tasks. In Proceeding of the Eleventh National Conference on Artificial Intelligence, 1993, AAAI Press/MIT Press, p.811-816.
[23] Ellen Riloff. Automatically Generating Extraction Pattern from Untagged Text. In Proceeding of the Thirteenth Conference on Artificial Intelligence (AAAI96), pp. 1044-1049.
[24] Ellen Riloff. Information Extraction as a stepping stone toward story understanding. In Understanding Language: Computational Models of Reading, edited by Ashwin Ram and Kenneth Moorman, The MIT Press.
[25] Soderland, S.; Fisher, D.; Aseltine, J.; and Lehnert, W. Crystal: Inducing a conceptual dictionary. Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI-95),p.1314-1319.
[26] Soderland, S. Learning to extract text-based information from the world wide web. Proceedings of Third International Conference on Knowledge Discovery and Data Mining,KDD97.
[27] R. Yangarber, Ralph Grishman, Pasi Tapanainen, and Silja Huttunen. Unsupervised discovery of scenario-level patterns for information extraction. Applied Natural Language Processing, 2000.
指導教授 張嘉惠(Chia-Hui Chang) 審核日期 2002-7-16
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