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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李允中 | |
| dc.contributor.author | Po-Yee Liu | en |
| dc.contributor.author | 劉鎛漪 | zh_TW |
| dc.date.accessioned | 2021-06-17T04:41:06Z | - |
| dc.date.available | 2019-08-08 | |
| dc.date.copyright | 2018-08-08 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-08-06 | |
| dc.identifier.citation | [1] Penn treebank, [online] https://web.archive.org/web/19970614160127/ http://www.cis.upenn.edu/˜treebank/. 2018.
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Barbosa. Robust entity linking via random walks. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pages 499–508. ACM, 2014. [18] R. Jenatton, N. L. Roux, A. Bordes, and G. R. Obozinski. A latent factor model for highly multi-relational data. In Advances in Neural Information Processing Systems, pages 3167–3175, 2012. [19] C. D. Manning, M. Surdeanu, J. Bauer, J. Finkel, S. J. Bethard, and D. McClosky. The Stanford CoreNLP natural language processing toolkit. In Association for Computational Linguistics (ACL) System Demonstrations, pages 55–60, 2014. [20] D. Milne and I. H. Witten. Learning to link with wikipedia. In Proceedings of the 17th ACM conference on Information and knowledge management, pages 509–518. ACM, 2008. [21] M. Nickel, K. Murphy, V. Tresp, and E. Gabrilovich. A review of relational machine learning for knowledge graphs. Proceedings of the IEEE, 104(1):11–33, 2016. [22] H. Sayyadi, M. Hurst, and A. Maykov. 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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70854 | - |
| dc.description.abstract | 新聞在生活中扮演重要的角色，而新聞中的事件卻是一個抽象的概念，我們定義事件是一群實體的集合。利用 Wikipedia 的資訊，我們整合三個 entity linking 系統 Wiki, PBoH 和 RandomWalk，並將新聞中的實體名字對應到 Wikipedia 的實體。並使用 TransE 模型將知識圖譜向量化，保存實體和實體之間的關係。最後，我們從新聞中建立一實體圖，用辨識圖型中重疊社群 (overlapping community) 的方法，使用 Nonnegative Matrix Factorization 的模型，辨識出新聞中的事件。 | zh_TW |
| dc.description.abstract | Nowadays, news is an important part of our daily life. But the event in news is an abstract concept. We define an event as a group of entities. By leveraging the information from Wikipedia, we integrate three entity linking system Wiki, PBoH and RandomWalk to link the entity mention in documents to correspondent entities in Wikipedia. We also use TransE model to embed each entity as a vector, which contains the relational information between entities. Finally, we build an entity graph from news document and use Nonnegative Matrix Factorization approach to find the overlapping community in our entity graph. The community is a group of vertices(entities) in the entity graph, which corresponds to our entity definition. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T04:41:06Z (GMT). No. of bitstreams: 1 ntu-107-R05922118-1.pdf: 1889505 bytes, checksum: b5dbfd3342fff3c3f9904557f6898c30 (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 致謝 ii
摘要 iii Abstracts iv List of Figures viii List of Tables ix Chapter 1 Introduction 1 Chapter 2 Related Work 4 2.1 Stanford Core NLP . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1.1 Part-Of-Speech Tagger . . . . . . . . . . . . . . . . . . . . . . 4 2.1.2 Dependency Parser . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.3 Named Entity Recognizer . . . . . . . . . . . . . . . . . . . . 5 2.1.4 Coreference Resolution . . . . . . . . . . . . . . . . . . . . . . 5 2.1.5 Open Information Extraction . . . . . . . . . . . . . . . . . . 5 2.2 Entity Linking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.1 Task Description . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.2 Knowledge Base . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.3 Entity Linking Modules . . . . . . . . . . . . . . . . . . . . . 7 2.3 Knowledge Graph Embedding . . . . . . . . . . . . . . . . . . . . . . 8 2.3.1 Translating Embeddings . . . . . . . . . . . . . . . . . . . . . 9 2.4 Community Detection . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.4.1 Overlapping Community Detection . . . . . . . . . . . . . . . 10 2.4.2 Nonnegative Matrix Factorization . . . . . . . . . . . . . . . . 11 Chapter 3 News Pipeline 12 Chapter 4 Parse News Document 14 Chapter 5 Entity Linking 15 5.1 Candidate Entity Generation . . . . . . . . . . . . . . . . . . . . . . 16 5.2 Candidate Entity Ranking . . . . . . . . . . . . . . . . . . . . . . . . 17 5.3 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 5.3.1 Benchmark . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 5.3.2 Evaluation Matrics . . . . . . . . . . . . . . . . . . . . . . . . 24 5.3.3 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 5.3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Chapter 6 Entity Embedding 27 6.1 Establish Knowledge Graph . . . . . . . . . . . . . . . . . . . . . . . 27 6.2 Knowledge Graph Embedding . . . . . . . . . . . . . . . . . . . . . . 28 6.3 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Chapter 7 Event Discovery 31 7.1 Build Entity Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 7.2 Community Detection . . . . . . . . . . . . . . . . . . . . . . . . . . 32 7.2.1 Event Discovery Demonstration . . . . . . . . . . . . . . . . . 33 7.2.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Chapter 8 Conclusion 34 Bibliography 36 | |
| dc.language.iso | zh-TW | |
| dc.subject | 新聞 | zh_TW |
| dc.subject | 事件 | zh_TW |
| dc.subject | 實體連結 | zh_TW |
| dc.subject | 知識圖譜向量化 | zh_TW |
| dc.subject | 社群辨識 | zh_TW |
| dc.subject | knowledge graph embedding | en |
| dc.subject | event | en |
| dc.subject | entity linking | en |
| dc.subject | community detection | en |
| dc.subject | news | en |
| dc.title | 使用事件向量解析新聞 | zh_TW |
| dc.title | Deciphering News with Event Embedding | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 鄭有進,郭忠義,李信杰,薛念林 | |
| dc.subject.keyword | 新聞,事件,實體連結,知識圖譜向量化,社群辨識, | zh_TW |
| dc.subject.keyword | news,event,entity linking,knowledge graph embedding,community detection, | en |
| dc.relation.page | 39 | |
| dc.identifier.doi | 10.6342/NTU201802570 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-08-06 | |
| 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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