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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 劉長遠(Cheng-Yuan Liou) | |
dc.contributor.author | Min-Cheng Wu | en |
dc.contributor.author | 吳旻誠 | zh_TW |
dc.date.accessioned | 2021-06-15T16:48:57Z | - |
dc.date.available | 2020-08-16 | |
dc.date.copyright | 2015-08-16 | |
dc.date.issued | 2015 | |
dc.date.submitted | 2015-08-07 | |
dc.identifier.citation | [1] Y. Bengio, R. Ducharme, P. Vincent. A neural probabilistic language model. Journal of Machine Learning Research, 3:1137-1155, 2003.
[2] Socher, R., Huang, E. H., Pennington, J., Ng, A. Y., and Manning,C. D. (2011a). Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In NIPS’2011. [3] Quoc V. Le and Tomas Mikolov. Distributed Representations of Sentences and Documents. International Conference on Machine Learning (ICML). 2014 [4] Mikolov, Tomas, et al. ”Recurrent neural network based language model.” INTERSPEECH.2010. [5] Ulrich J., Murray G., Carenini G., A Publicly Available Annotated Corpus for Supervised Email Summarization AAAI08 EMAIL Workshop, Chicago, USA, 2008. [6] Ulrich, J., Carenini, G., Murray, G., Ng, R.: Regression-based summarization of email conversations.In: 3rd Int’l AAAI Conference on Weblogs and Social Media (ICWSM 2009),San Jose, CA. AAAI, Menlo Park. 2009 [7] Rambow, O., Shrestha, L., Chen, J., Lauridsen, C.: Summarizing email threads. In: HLTNAACL 2004: Proceedings of HLT-NAACL 2004: Short Papers on XX,Morristown, NJ,USA, pp. 105– 108. Association for Computational Linguistics.2004 [8] Carenini, G., Ng, R.T., Zhou, X.: Summarizing emails with conversational cohesion and subjectivity. In: Proceedings of ACL 2008: HLT, Columbus, Ohio, pp. 353– 361. Association for Computational Linguistics. 2008 [9] Muresan, S., Tzoukermann, E., and Klavans, J. L.Combining linguistic and machine learning techniques for email summarization.Workshop at ACL/ EACL 2001 Conference. 2001 [10] Lin, C. (2004). Rouge: a package for automatic evaluation of summaries.Proceedings of the Workshop on Text Summarization Branches Out.2004 [11] J. Elman. Finding Structure in Time. Cognitive Science, 14, 179-211, 1990. [12] Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011). Learning Word Vectors for Sentiment Analysis. The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011). [13] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. Distributed Representations of Words and Phrases and their Compositionality. In Proceedings of NIPS, 2013. [14] F. Morin, Y. Bengio. Hierarchical Probabilistic Neural Network Language Model. AISTATS,2005. [15] L. Shrestha and K. McKeown. Detection of question-answer pairs in email conversations. In Proc. of COLING, 2004. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/53174 | - |
dc.description.abstract | 本論文提出了兩個自動郵件摘要的模型,其一是延伸了過去既有的
使用機器學習方法的自動郵件摘要模型,我們以類神經網路來改善其 特徵抽取的方法,並提出了新的郵件摘要特徵,在同樣使用支援向量 迴歸模型來進行迴歸分析的實驗下,此模型有效的增進了既有模型的 效能。 另外我們提出了完全使用類神經網路進行郵件摘要的模型,此模型基 於以類神經網路自動抽取出的文句特徵,並以遞迴式類神經網路來模 擬人類選取摘要時所進行的動作,此模型雖在實驗中表現效能較差, 但其不需要任何的人類定義特徵,完全使用類神經網路完成自動郵件 摘要的動作。 | zh_TW |
dc.description.abstract | In this thesis , we use recent neural network sentence representation technique
and propose some new feature in the email summarization research area to improve the performance of email summarization task , and we proposed a new neural network summarization model that imitate the prodcedure of human summarizing the document , then we compare the result of these models. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T16:48:57Z (GMT). No. of bitstreams: 1 ntu-104-R02922136-1.pdf: 691496 bytes, checksum: 1385784bc2a7a5b20fdeb46645342d2b (MD5) Previous issue date: 2015 | en |
dc.description.tableofcontents | 口試委員審定書 i
摘要ii Abstract iii Contents iv List of Figures vi List of Tables vii 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Email Summarization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.3 Neural Network Sentence Feature Extraction . . . . . . . . . . . . . . . 2 1.4 Evaluation Metrics for Email Summarization Task . . . . . . . . . . . . . 4 1.4.1 Rouge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4.2 Weighted Recall . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 Improving Regression Based Email Summarization By Neural Networks 8 2.1 Paragraph Vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.1.1 Hierarchical Softmax . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1.2 Negative Sampling . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.1.3 Continous Bag of Words Model(CBOW) . . . . . . . . . . . . . 11 2.1.4 Continous Skip-Gram Model . . . . . . . . . . . . . . . . . . . . 15 2.2 Linguistic Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.2.1 Follow Quote Feature . . . . . . . . . . . . . . . . . . . . . . . 17 2.2.2 Cue Phrases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.2.3 Title Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.2.4 Topic Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.5 Similarity to the neighbor sentence . . . . . . . . . . . . . . . . . 18 2.3 Modify Clue Word Summarization . . . . . . . . . . . . . . . . . . . . . 18 2.3.1 Construct the fragment quotation graph . . . . . . . . . . . . . . 19 2.3.2 Construct Sentence Graph . . . . . . . . . . . . . . . . . . . . . 20 2.4 Question Describe Feature . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.4.1 Question Detection . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.4.2 Question Answer Feature . . . . . . . . . . . . . . . . . . . . . . 23 2.4.3 Sentiment Feature . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.5 Regression Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.6 Recurrent Neural Network Summarization Model . . . . . . . . . . . . . 24 3 Experiments 26 3.1 Bc3 corpus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.2 Experiment Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4 Conclusion 28 5 Summary of three models 29 v | |
dc.language.iso | en | |
dc.title | 使用類神經網路增進自動郵件摘要效能之研究 | zh_TW |
dc.title | Improving Email Summarization By Neural Networks | en |
dc.type | Thesis | |
dc.date.schoolyear | 103-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 呂育道(Yuh-Dauh Lyuu),劉俊緯(Jiun-Wei Liou) | |
dc.subject.keyword | 自動郵件摘要,類神經網路,遞迴式類神經網路,機器學習,自然語言處理, | zh_TW |
dc.subject.keyword | Email Summarization,Neural Network,Recurrent Neural Network,Machine Learning,Natural Language Processing, | en |
dc.relation.page | 41 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2015-08-07 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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