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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳建錦(Chien Chin Chen) | |
| dc.contributor.author | Yung-Chun Chang | en |
| dc.contributor.author | 張詠淳 | zh_TW |
| dc.date.accessioned | 2021-06-15T13:02:04Z | - |
| dc.date.available | 2017-05-13 | |
| dc.date.copyright | 2016-07-26 | |
| dc.date.issued | 2016 | |
| dc.date.submitted | 2016-07-11 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/50848 | - |
| dc.description.abstract | 一個主題事件是由特定的時間、地點以及人物所構成，因此探索主題事件中人與人的互動關係能夠協助使用者建構起該主題事件的背景知識，進而快速理解該主題文章中所描述的內容。不同於以往的研究，本研究所進行的主題人物互動關係研究並不局限於一特定關係，或尋找一特定關係的關係擷取規則，且此互動關係是會隨著主題事件的不同而改變。在探索某主題下之人物互動關係時，主題文章透過互動關係偵測方法分割成多個片段，並且判斷哪些片段是含有主題人物互動關係存在，接著應用資訊擷取演算法從互動文句片段中抽取互動關係的組成元素，進而建構出主題人物互動網絡。
本論文將辨識互動文句片段辨識視為一個分類的問題，為了探索不同知識對辨識主題人物互動關係之效益，提出了一個以特徵為基礎的互動文句片段辨識方法，該方法共包含了19種不同特徵知識，考量了語法結構、文章脈絡以及語意資訊。在探索不同知識對本研究之效益後，為了能有效地表達與整合不同知識之結構，本論文進而提出了一個豐富互動樹狀結構，透過此樹狀結構有效地整合句法結構、文章脈絡以及語意資訊，並透過樹狀核心方法學習該結構，藉此捕捉人際互動關係描述。本研究蒐集大量的新聞事件進行效能評估，根據實驗結果顯示，結合不同的特徵知識能夠有效地提升辨識人際互動關係之效能，且本研究所提出的豐富互動樹狀結構能有效地偵測主題人物互動，而其效能也優於其他的比較方法；此外，根據案例分析之結果顯示，本研究方法確實能有效地建構主題人物互動關係網絡，此一網絡呈現出人際互動關係之多樣且變動之特性，並能提供讀者快速掌握該主題之背景知識。 | zh_TW |
| dc.description.abstract | The development of a topic in a set of topic documents is constituted by a series of person interactions at a specific time and place. Knowing the interactions of the persons mentioned in these documents is helpful for readers to better comprehend the documents. To discover person interactions, we need a detection method that can identify text segments containing information about the interactions. Information extraction algorithms then analyse the segments to extract interaction tuples and construct an interaction network of topic persons.
In this dissertation, we define interaction detection as a classification problem. We first recognize person interactions from topic documents by exploring various types of knowledge. We present a feature-based approach called FISER, exploits 19 features covering syntactic, context-dependent, and semantic information in text to detect person interactions. Then, we design the rich interactive tree structure to represent syntactic, context, and semantic information of text, and this structure is incorporated into a tree-based convolution kernel to identify interactive segments. Experiment results based on real world topics demonstrate that effective incorporation of divers features enable our system recognize person interactions efficiently. Moreover, the proposed rich interactive tree structure effectively detects the topic person interaction and that our method outperforms many well-known relation extraction and protein-protein interaction methods. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T13:02:04Z (GMT). No. of bitstreams: 1 ntu-105-D98725007-1.pdf: 3972138 bytes, checksum: cba1b30251b4d18f5d533999ab6621a2 (MD5) Previous issue date: 2016 | en |
| dc.description.tableofcontents | 口試委員會審定書 I
謝辭 II 中文摘要 III 英文摘要 VI Chapter 1 Introduction 1 1.1 An Introduction to Person Interaction Recognization 1 1.2 Motivation 4 1.3 The Organization of This Dissertation 6 Chapter 2 Related Works 8 2.1 Relation Extraction 8 2.2 Open Information Extraction 11 2.3 Protein-protein Interaction Detection 13 Chapter 3 A Feature-based Detection System for Person Interactions 16 3.1 Candidate Segment Generation 17 3.2 Interactive Segment Recognizer 19 3.3 Feature Extraction 21 Chapter 4 Experimental Results of FISER 27 4.1 Corpus and Evaluation Metric 27 4.2 Effects of the Features 31 4.3 The Best Combination of the Features 35 4.4 The Effectiveness of the Features 37 4.5 Comparison with Open IE Methods 41 Chapter 5 A Tree Kernel-based Method for Person Interaction Detection 46 5.1 Candidate Segment Generation 47 5.2 Segment Structure Generalization 49 5.3 Rich Interactive Tree Construction 52 5.3.1 RIT Branching 53 5.3.2 RIT Pruning 53 5.3.3 RIT Ornamenting 57 5.4 Convolution Tree Kernel Classification 60 Chapter 6 Experimentation for SPIRIT 63 6.1 Evaluation Dataset and Experiment Setting 63 6.2 System Component Evaluation 65 6.2.1 Performance Evaluation for Interaction Detection 65 6.3 Case Studies: Topic Person Interaction Network 86 Chapter 7 Conclusions Remarks and Future Works 91 Reference 95 | |
| dc.language.iso | en | |
| dc.subject | 主題人物互動關係擷取 | zh_TW |
| dc.subject | 主題人物互動關係偵測 | zh_TW |
| dc.subject | 主題摘要 | zh_TW |
| dc.subject | 文字探勘 | zh_TW |
| dc.subject | 主題人物互動網絡 | zh_TW |
| dc.subject | 主題人物互動關係偵測 | zh_TW |
| dc.subject | 主題人物互動關係擷取 | zh_TW |
| dc.subject | 主題摘要 | zh_TW |
| dc.subject | 文字探勘 | zh_TW |
| dc.subject | 主題人物互動網絡 | zh_TW |
| dc.subject | Interaction Detection | en |
| dc.subject | Topic Person Interaction Network | en |
| dc.subject | Interaction Extraction | en |
| dc.subject | Interaction Detection | en |
| dc.subject | Topic Summarization | en |
| dc.subject | Text Mining | en |
| dc.subject | Topic Person Interaction Network | en |
| dc.subject | Interaction Extraction | en |
| dc.subject | Text Mining | en |
| dc.subject | Topic Summarization | en |
| dc.title | 主題人物互動網絡建構之研究 | zh_TW |
| dc.title | A Study of Constructing Topic Person Interaction
Network | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 104-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.coadvisor | 許聞廉(Wen-Lian Hsu) | |
| dc.contributor.oralexamcommittee | 陳信希(Hsin-Hsi Chen),魏志平(Chih-Ping Wei),陳孟彰(Meng Chang Chen),古倫維(Lun-Wei Ku) | |
| dc.subject.keyword | 文字探勘,主題摘要,主題人物互動關係偵測,主題人物互動關係擷取,主題人物互動網絡, | zh_TW |
| dc.subject.keyword | Text Mining,Topic Summarization,Interaction Detection,Interaction Extraction,Topic Person Interaction Network, | en |
| dc.relation.page | 105 | |
| dc.identifier.doi | 10.6342/NTU201600791 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2016-07-11 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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