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    政大機構典藏 > 理學院 > 資訊科學系 > 學位論文 >  Item 140.119/55034
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/55034

    Title: 英文介系詞片語定位與英文介系詞推薦
    Attachment of English prepositional phrases and suggestions of English prepositions
    Authors: 蔡家琦
    Tsai, Chia Chi
    Contributors: 劉昭麟
    Liu, Chao Lin
    Tsai, Chia Chi
    Keywords: 語義分析
    semantic analysis
    machine translation
    text proofreading
    Date: 2011
    Issue Date: 2012-10-30 15:21:59 (UTC+8)
    Abstract: 英文介系詞在句子裡所扮演的角色通常是用來使介系詞片語更精確地補述上下文,英文的母語使用者可以很直覺地使用。然而電腦不瞭解語義,因此不容易判斷介系詞修飾對象;非英文母語使用者則不容易直覺地使用正確的介系詞。所以本研究將專注於介系詞片語定位與介系詞推薦的議題。
    藉由使用真實生活語料,我們的方法處理介系詞片語定位的問題,比同樣考慮四個中心詞的最大熵值法(Max Entropy)好;但與考慮上下文的Stanford剖析器差不多。而在介系詞推薦的問題裡,較難有全面比較的對象,但我們的方法精準度可達到53.14%。
    This thesis focuses on problems of attachment of prepositional phrases (PPs) and problems of prepositional suggestions. Determining the correct PP attachment is not easy for computers. Using correct prepositions is not easy for learners of English as a second language.
    I transform the problems of PPs attachment and prepositional suggestion into an abstract model, and apply the same computational procedures to solve these two problems. The common model features four headwords, i.e., the verb, the first noun, the preposition, and the second noun in the prepositional phrases. My methods consider the semantic features of the headwords in WordNet to train classification models, and apply the learned models for tackling the attachment and suggestion problems. This exploration of PP attachment problems is special in that only those PPs that are almost equally possible to attach to the verb and the first noun were used in the study.
    The proposed models consider only four headwords to achieve satisfactory performances. In experiments for PP attachment, my methods outperformed a Maximum Entropy classifier which also considered four headwords. The performances of my methods and of the Stanford parsers were similar, while the Stanford parsers had access to the complete sentences to judge the attachments. In experiments for prepositional suggestions, my methods found the correct prepositions 53.14% of the time, which is not as good as the best performing system today.
    This study reconfirms that semantic information is instrument for both PP attachment and prepositional suggestions. High level semantic information helped to offer good performances, and hierarchical semantic synsets helped to improve the observed results. I believe that the reported results are valuable for future studies of PP attachment and prepositional suggestions, which are key components for machine translation and text proofreading.
    Reference: [1] Eneko Agirre, Timothy Baldwin, and David Martinez. Improving Parsing and PP Attachment Performance with Sense Information. In 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2008.
    [2] Michaela Atterer and Hinrich Schütze. Prepositional Phrase Attachment without Oracles. Computational Linguistics, 33(4):469–476, 2007.
    [3] Timothy Baldwin, Valia Kordoni, and Aline Villavicencio. Prepositions in Applications: A Survey and Introduction to the Special Issue. Computational Linguistics, 35(2):119–149, 2009.
    [4] Michael John Collins. Head-driven Statistical Models for Natural Language Parsing. PhD thesis, 1999.
    [5] Gregory F. Coppola, Alexandra Birch, Tejaswini Deoskar, and Mark Steedman. Simple Semi-supervised Learning for Prepositional Phrase Attachment. In Proceedings of the 12th International Conference on Parsing Technologies, pages 129–139, 2011.
    [6] RacheleDeFeliceandStephenG.Pulman.AutomaticallyAcquiringModelsofPreposition Use. In Proceedings of the Fourth ACL-SIGSEM Workshop on Prepositions, pages 45–50, 2007.
    [7] Rachele De Felice and Stephen G. Pulman. A Classifier-based Approach to Preposition and Determiner Error Correction in L2 English. In Proceedings of the 22nd International Conference on Computational Linguistics, volume 1, pages 169–176, 2008.
    [8] Michael Gamon, Jianfeng Gao, Chris Brockett, and Re Klementiev. Using Contextual Speller Techniques and Language Modeling for ESL Error Correction. In Proceedings of Joint Conference on Natural Language Processing 2008, pages 449–456, 2008.
    [9] Na-Rae Han, Joel Tetreault, Soo-Hwa Lee, and Jin-Young Ha. Using an Error-annotated Learner Corpus to Develop an ESL/EFL Error Correction System. In Proceedings of the Seventh conference on International Language Resources and Evaluation, 2010.
    [10] Donald Hindle and Mats Rooth. Structural Ambiguity and Lexical Relations. Computational Linguistics, 19(1):103–120, 1993.
    [11] Dirk Hovy, Stephen Tratz, and Eduard Hovy. What’s in a Preposition?: Dimensions of Sense Disambiguation for an Interesting Word Class. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pages 454–462, 2010.
    [12] Dan Klein and Christopher D. Manning. Fast Exact Inference with a Factored Model for Natural Language Parsing. In Advances in Neural Information Processing Systems, volume 15, pages 3–10, 2003.
    [13] Claudia Leacock, Michael Gamon, and Chris Brockett. User Input and Interactions on Microsoft Research ESL Assistant. In Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications, pages 73–81, 2009.
    [14] Ken C. Litkowski and Orin Hargraves. Coverage and Inheritance in The Preposition Project. In Proceedings of the Third ACL-SIGSEM Workshop on Prepositions, pages 37– 44, 2006.
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    [16] Tom O’Hara and Janyce Wiebe. Exploiting Semantic Role Resources for Preposition Disambiguation. Computational Linguistics, 35(2):151–184, 2009.
    [17] Marian Olteanu and Dan Moldovan. PP-Attachment Disambiguation Using Large Context. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pages 273–280, 2005.
    [18] Patrick Pantel and Dekang Lin. An Unsupervised Approach to Prepositional Phrase Attachment Using Contextually Similar Words. In Proceedings of the 38th Annual Meeting on Association for Computational Linguistics, pages 101–108, 2000.
    [19] Li Quan, Oleksandr Kolomiyets, and Marie-Francine Moens. KU Leuven at HOO-2012: A Hybrid Approach to Detection and Correction of Determiner and Preposition Errors in Non-native English Text. In Proceedings of the Seventh Workshop on Building Educational Applications Using NLP, pages 263–271, 2012.
    [20] Adwait Ratnaparkhi, Jeff Reynar, and Salim Roukos. A Maximum Entropy Model for Prepositional Phrase Attachment. In Proceedings of the Workshop on Human Language Technology, pages 250–255, 1994.
    [21] Jiri Stetina and Makoto Nagao. Corpus Based PP Attachment Ambiguity Resolution with a Semantic Dictionary. In Proceedings of the Fifth Workshop on Very Large Corpora, pages 66–80, 1997.
    [22] JoelR.TetreaultandMartinChodorow.TheUpsandDownsofPrepositionErrorDetection in ESL Writing. In Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1, pages 865–872, 2008.
    [23] Stephen Tratz and Dirk Hovy. Disambiguation of Preposition Sense Using Linguistically Motivated Features. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium, pages 96–100, 2009.
    [24] Martin Volk. Combining Unsupervised and Supervised Methods for PP Attachment Disambiguation. In Proceedings of the 19th International Conference on Computational Linguistics, volume 1, pages 1–7, 2002.
    [25] Jian-Cheng Wu, Joseph Chang, Yi-Chun Chen, Shih-Ting Huang, Mei-Hua Chen, and Jason S. Chang. Helping Our Own: NTHU NLPLAB System Description. In Proceedings of the Seventh Workshop on Building Educational Applications Using NLP, pages 295–301, 2012.
    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0099753006
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

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