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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 鄭卜壬(Pu-Jen Cheng) | |
dc.contributor.author | Hsin-Chung Lin | en |
dc.contributor.author | 林信仲 | zh_TW |
dc.date.accessioned | 2021-06-13T15:22:00Z | - |
dc.date.available | 2009-07-23 | |
dc.date.copyright | 2008-07-23 | |
dc.date.issued | 2008 | |
dc.date.submitted | 2008-07-21 | |
dc.identifier.citation | [1] Alekh Agarwal and Soumen Chakrabarti , Learning Random Walks to Rank Nodes in Graphs. In Proceedings of the 24th international conference on Machine learning (ICML), 2007 .
[2] Douglas E. Appelt and David J. Israel, Introduction to Information Extraction Technology. A Tutorial Prepared for IJCAI-99 [3] Roberto J. Bayardo, Ramakrishnan and Srikant, Scaling Up All Pairs Similarity Search. In Proceedings of the 16th international conference on World Wide Web (WWW),2007. [4] R. Bunescu and R. J. Mooney. Collective information extraction with relational Markov networks. In Proceedings of the 42nd ACL, 2004. [5] K. C.-C. Chang, B. He, and Z. Zhang, Toward Large Scale Integration: Building a MetaQuerier over Databases on the Web. In CIDR, 2005. [6] Soumen Chakrabarti, Dynamic Personalized Pagerank in EntityRelation Graphs. In Proceedings of the 16th international conference on World Wide Web (WWW),2007. [7] Soumen Chakrabarti , Alekh Agarwal , Sunny Aggarwal , Learning to Rank Networked Entities. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006. [8] Soumen Chakrabarti and Alekh Agarwal, Learning Parameters in Entity Relationship Graphs from Ranking Preferences. In 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), 2006 . [9] Soumen Chakrabarti, Kriti Puniyani and Sujatha Das., Optimizing scoring functions and indexes for proximity search in type-annotated corpora. In Proceedings of the 15th international conference on World Wide Web (WWW),2006. [10] T. Cheng and K. C.-C. Chang. Entity search engine: Towards agile best-effort information integration over the web. In CIDR, pages 108.113, 2007. [11] T. Cheng, X. Yan, and K. C.-C. Chang. EntityRank: Searching Entities Directly and Holistically. In Proceedings of the 33rd Very Large Data Bases Conference (VLDB ), 2007. [12] T. Cheng, X. Yan, and K. C.-C. Chang. Supporting entity search: A large-scale prototype search system. In Proceedings of the 2007 ACM SIGMOD international conference on Management of data, 2007. [13] H. L. Chieu and H. T. Ng. Named entity recognition: a maximum entropy approach using global information. In Proceedings of the 19th Coling, 2002. [14] J. R. Curran and S. Clark. Language independent NER using a maximum entropy tagger. In Proceedings of the 7th CoNLL, 2003. [15] Jenny Rose Finkel, Trond Grenager, and Christopher Manning. Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling. Proceedings of the 43nd Annual Meeting of the Association for Computational Linguistics (ACL), 2005. [16] J. Finkel, S. Dingare, H. Nguyen, M. Nissim, and C. D. Manning. Exploiting context for biomedical entity recognition: from syntax to the web. In JointWorkshop on Natural Language Processing in Biomedicine and Its Applications at Coling 2004. [17] G Hu, J Liu, H Li, Y Cao, JY Nie, J Gao , A Supervised Learning Approach to Entity Search. In AIRS, 2006. [18] R. Malouf. Markov models for language-independent named entity recognition. In Proceedings of the 6th CoNLL, 2002. [19] A. Mikheev, M. Moens, and C. Grover. Named entity recognition without gazetteers. In Proceedings of the 9th EACL,1999. [20] Dawei Song and Peter Bruza, Discovering information flow suing high dimensional conceptual space. In Proceedings of the 24th annual international ACM SIGIR conference, 2001. [21] C. Sutton and A. McCallum. Collective segmentation and labeling of distant entities in information extraction. In ICML Workshop on Statistical Relational Learning and Its connections to Other Fields. 2004. [22] B. Taskar, P. Abbeel, and D. Koller. Discriminative probabilistic models for relational data. In Proceedings of the 18th Conference on Uncertianty in Artificial Intelligence (UAI), 2002. [23] X Wang, JT Sun, and Z Chen, Shine: search heterogeneous interrelated entities. In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management (CIKM), 2007 [24] X Wu, L Zhang, and Y Yu ,Exploring social annotations for the semantic web. In Proceedings of the 15th international conference on World Wide Web (WWW), 2006. [25] Stanford NER. http://nlp.stanford.edu/ner/index.shtml [26] Google Search Engine. http://www.google.com [27] The Lemur Toolkit for Language Modeling and Information Retrieval. http://www.lemurproject.org/ [28] Dmoz Open Directory Project, http://www.dmoz.org/ [29] Yahoo! Directory. http://dir.yahoo.com/ [30] WEB2DB. http://www.knowlesys.com/services.htm | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/37231 | - |
dc.description.abstract | 在本篇論文中,我們定義了以概念為基礎之實體相似度搜尋這個問題。在實體相似度搜尋中,我們輸入一個查詢詞以及所要搜尋的實體類型,這個搜尋系統會回覆一個經過排序後的列表,列表上有經過排序後的實體,其實體的形態為使用者輸入要查詢的型態。此列表排序的依據為跟查詢詞的相似度。在實體相似度搜尋中如何排序是一個關鍵的議題。在之前的文獻中,抽取實體的文獻是專注於如何抽取正確的實體,而實體排序的文獻則是專注於如何依據實體間的關切程度來排序實體。一般而言,還有許多其他的特徵可以用在排序實體相似度搜尋,而不是只有取決於上下文的特徵。我們提出一個可用在網上相似度搜尋的普遍架構。這個架構可以依據使用者的回饋來自動調整計算相似度的函式。我們對這個問題有一個假設就是在概念上實體之間會有語意上的關係。我們驗證我們的線上原型系統使用線上的搜集到的資料,而且證明我們的方法是能運作成功的。 | zh_TW |
dc.description.abstract | In this paper we address the problem of concept-based entity similarity search. In entity similarity search, given a query and an entity type, a search system returns a ranked list of entities in the type (e.g., person name, e-mail) relevant to the query. Ranking is a key issue in entity similarity search. In literature, entity extraction focuses on how to extract correct entities and entity ranking focuses on the ranking of entities according to the relevance between entities. In general, many features may be useful for ranking in entity similarity search no more than the contextual feature. We propose a general framework for entity similarity search on the web. And this framework is able to adjust the similarity function according to the user’s relevance feedback. The assumption of this problem, we propose there are semantic relationships among entities at conceptual level. We evaluate our online prototype over a Web corpus, and show that our approach performs effectively. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T15:22:00Z (GMT). No. of bitstreams: 1 ntu-97-R95944027-1.pdf: 1277594 bytes, checksum: f67aacb2c1a49f71b2781792d7aa218b (MD5) Previous issue date: 2008 | en |
dc.description.tableofcontents | 摘要........................................................................................................................................... ii
ABSTRACT .............................................................................................................................. iii Acknowledgements...................................................................................................................iv List of Figures ..........................................................................................................................vii List of Tables.......................................................................................................................... viii Chapter 1: Introduction.............................................................................................................1 1.1 Motivation........................................................................................................................1 1.2 Previous Work..................................................................................................................3 1.3 Basic Idea..........................................................................................................................3 1.4 Challenges........................................................................................................................4 1.6 Experiments.....................................................................................................................4 1.7 Contributions...................................................................................................................4 Chapter 2: Related Work...........................................................................................................5 2.1 Entity Extraction .......................................................................................................5 2.2 Entity Ranking...........................................................................................................7 2.3 Conceptual Space Model............................................................................................8 Chapter 3: The Problem ............................................................................................................9 Chapter 4: Our Approach....................................................................................................... 13 4.1 Overview................................................................................................................. 14 4.2 Entity Extraction .................................................................................................... 14 4.3 Feature Extraction.................................................................................................. 18 4.3.1 Weight of the Entity-Type Features ....................................................................... 18 4.3.2 Extract Taxonomy-Type Features.......................................................................... 20 4.3.3 Extract Web-Type Features................................................................................... 25 4.4 Learning Similarity Function ................................................................................. 32 4.4.1 Similarity measure functions mijk........................................................................... 35 4.4.2 Update of ij W ...................................................................................................... 35 4.4.3 Update of ijk W ..................................................................................................... 37 Chapter 5: Experiments .......................................................................................................... 39 5.1 Overview ................................................................................................................. 39 5.2 Data Set ................................................................................................................... 39 5.3 Entity Search ............................................................................................................ 39 5.4 Relevance Feedback ................................................................................................. 42 Chapter 6: Discussion ............................................................................................................. 46 Chapter 7: Applications .......................................................................................................... 48 7.1 Recommender System .............................................................................................. 48 7.2 Cluster ..................................................................................................................... 49 Chapter 8: Conclusion and Future work ................................................................................ 50 References ............................................................................................................................... 51 | |
dc.language.iso | en | |
dc.title | 以概念為基礎之實體相似度搜尋 | zh_TW |
dc.title | Concept-based Entity Similarity Search | en |
dc.type | Thesis | |
dc.date.schoolyear | 96-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 王正豪,邱志義 | |
dc.subject.keyword | 實體搜尋,實體相似度, | zh_TW |
dc.subject.keyword | Entity Search,Entity Similarity, | en |
dc.relation.page | 53 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2008-07-23 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
顯示於系所單位: | 資訊網路與多媒體研究所 |
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