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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳信希 | |
dc.contributor.author | Chuan-An Lin | en |
dc.contributor.author | 林傳恩 | zh_TW |
dc.date.accessioned | 2021-05-12T09:36:20Z | - |
dc.date.available | 2018-08-22 | |
dc.date.available | 2021-05-12T09:36:20Z | - |
dc.date.copyright | 2018-08-22 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-08-22 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/handle/123456789/1321 | - |
dc.description.abstract | 中文語篇剖析有四項子任務,包含初級語篇單元分割、剖析樹建立、主次關係識別、語篇關係辨識等。本文展示一個點對點中文語篇剖析器,並提出一套統一架構,可以對輸入之中文篇章直接產生完整的中文語篇剖析結果。我們的剖析器以遞迴類神經網路為基礎,同時對四項子任務進行學習,在中文語篇樹庫(CDTB)資料集上,達到最先進的效能。我們釋出了這個剖析器的原始碼與預先訓練完成的模型,立即可用。據我們所知,這是第一個開放原始碼的中文剖析工具集,而且這套獨立的工具集不須依賴外部資源(如句法剖析器),便於下游應用的整合。 | zh_TW |
dc.description.abstract | This paper demonstrates an end-to-end Chinese discourse parser. We propose a unified framework based on recursive neural network (RvNN) to jointly model the subtasks including elementary discourse unit (EDU) segmentation, tree structure construction, center labeling, and sense labeling. Experimental results show our parser achieves the state-of-the-art performance in the Chinese Discourse Treebank (CDTB) dataset. We release the source code with a pre-trained model for the NLP community. To the best of our knowledge, this is the first open source toolkit for Chinese discourse parsing. The standalone toolkit can be integrated into subsequent applications without the need of external resources such as syntactic parser. | en |
dc.description.provenance | Made available in DSpace on 2021-05-12T09:36:20Z (GMT). No. of bitstreams: 1 ntu-107-R05922055-1.pdf: 2575360 bytes, checksum: c43120bb8f6af79452c3202bda3e2cea (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 口試委員會審定書iii
誌謝v 摘要vii Abstract ix 1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Discourse parsing and its application . . . . . . . . . . . . . . . 1 1.1.2 Task Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2 Related Work 7 2.1 English Discourse Corpora . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 English Discourse Research . . . . . . . . . . . . . . . . . . . . . . . . 8 2.3 Chinese Discourse Corpora . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.4 Chinese Discourse Research . . . . . . . . . . . . . . . . . . . . . . . . 9 2.5 Recurrent Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.6 Recursive Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . 10 3 Datasets 13 3.1 Chinese Discourse Treebank . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2 Chinese Treebank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4 Methods 20 4.1 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.2 Recursive Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.3 Text Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.4 Handling Binary Tree Structure . . . . . . . . . . . . . . . . . . . . . . . 23 4.5 Parser Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.6 Parse Tree Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.7 Model Variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 5 Experiments 30 5.1 Evaluation Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 5.2 Gold EDU Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 5.3 End-to-End Parsing Experiment . . . . . . . . . . . . . . . . . . . . . . 33 5.4 Joint Parsing Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . 34 5.5 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 5.6 Case Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 6 Conclusion and Future Work 41 6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 6.2.1 Enhance the sense classifier and center classifier . . . . . . . . . 42 6.2.2 Fit the parsing model to the multi-way tree structure . . . . . . . 42 6.2.3 Integrate syntactic information to build a syntactic-discourse jointly parsing model . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 Bibliography 43 | |
dc.language.iso | en | |
dc.title | 適用於點對點中文語篇剖析的遞迴類神經網路統一架構 | zh_TW |
dc.title | A Unified RvNN Framework for End-to-End Chinese Discourse Parsing | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 蔡宗翰,蔡明峰 | |
dc.subject.keyword | 自然語言處理,中文語篇剖析,遞迴類神經網路,篇章結構,基本篇章單元, | zh_TW |
dc.subject.keyword | Natural Language Processing,Chinese Discourse Parsing,Recursive Neural Network,Discourse Structure,Elementary Discourse Unit, | en |
dc.relation.page | 47 | |
dc.identifier.doi | 10.6342/NTU201803852 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2018-08-22 | |
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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