Please use this identifier to cite or link to this item:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/84817Full metadata record
| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 魏治平(Chih-Ping Wei) | |
| dc.contributor.author | Chieh-Ting Yen | en |
| dc.contributor.author | 顏价廷 | zh_TW |
| dc.date.accessioned | 2023-03-19T22:27:11Z | - |
| dc.date.copyright | 2022-08-30 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-08-29 | |
| dc.identifier.citation | Blaschke, C., & Valencia, A. (2001). Can Bibliographic Pointers for Known Biological Data Be Found Automatically? Protein Interactions as a Case Study. Comparative and Functional Genomics, 2(4), 196–206. Bodenreider, O. (2004). The Unified Medical Language System (UMLS): Integrating biomedical terminology. Nucleic Acids Research, 32(Database issue), D267– D270. Chan, Y. S., & Roth, D. (2011). Exploiting Syntactico-Semantic Structures for Relation Extraction. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 551–560. Chang, C.-H. (2020). Biomedical Relation Extraction Supporting by Knowledge Graph Embedding Model. Unpublished Master Thesis, Department of Information Management, National Taiwan University, Taipei, Taiwan. Gupta, P., Schütze, H., & Andrassy, B. (2016). Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction. Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, 2537–2547. Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. Kilicoglu, Halil, Marcelo Fiszman, Alejandro Rodriguez, Dongwook Shin, Anna Ripple, and Thomas Rindflesch. 2007. “Semantic MEDLINE: A Web Application for Managing the Results of PubMed Searches.” Proceedings of the Third International Symposium on Semantic Mining in Biomedicine (SMBM 2008). Kilicoglu, H., Shin, D., Fiszman, M., Rosemblat, G., & Rindflesch, T. C. (2012). SemMedDB: A PubMed-scale repository of biomedical semantic predications. Bioinformatics (Oxford, England), 28(23), 3158–3160. Kingma, D. P., & Ba, J. (2017). Adam: A Method for Stochastic Optimization (arXiv:1412.6980). arXiv. Kuo Y.-T. (2019). Using Piecewise Convolutional Neural Networks for Biomedical Relation Extraction. Unpublished Master Thesis, Department of Information Management, National Taiwan University, Taipei, Taiwan. Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016). Neural Architectures for Named Entity Recognition (arXiv:1603.01360). arXiv. Levy, Omer, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017. “Zero-Shot Relation Extraction via Reading Comprehension.” ArXiv:1706.04115 [Cs]. Li, Q., & Ji, H. (2014). Incremental Joint Extraction of Entity Mentions and Relations. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 402–412. Li, X., Feng, J., Meng, Y., Han, Q., Wu, F., & Li, J. (2020). A Unified MRC Framework for Named Entity Recognition. ArXiv:1910.11476 [Cs]. Lindberg, D. A., B. L. Humphreys, and A. T. McCray. 1993. “The Unified Medical Language System.” Methods of Information in Medicine 32(4):281–91. Luo, L., Yang, Z., Cao, M., Wang, L., Zhang, Y., & Lin, H. (2020). A neural network- based joint learning approach for biomedical entity and relation extraction from biomedical literature. Journal of Biomedical Informatics, 103, 103384. Mintz, M., Bills, S., Snow, R., & Jurafsky, D. (2009). Distant supervision for relation extraction without labeled data. Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, 1003–1011. Miwa, M., & Sasaki, Y. (2014). Modeling Joint Entity and Relation Extraction with Table Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1858–1869. Ren, X., Wu, Z., He, W., Qu, M., Voss, C. R., Ji, H., Abdelzaher, T. F., & Han, J. (2017). CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases. Rindflesch, T. C., & Fiszman, M. (2003). The interaction of domain knowledge and linguistic structure in natural language processing: Interpreting hypernymic propositions in biomedical text. Journal of Biomedical Informatics, 36(6), 462– 477. Stone, M. 1974. “Cross-Validatory Choice and Assessment of Statistical Predictions.” Journal of the Royal Statistical Society. Series B (Methodological) 36(2):111– 47. Vaswani, A., Bisk, Y., Sagae, K., & Musa, R. (2016). Supertagging With LSTMs. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 232–237. Wang, T.-Y. (2021). “Beyond Extracting One-to-One Relations: Joint Biomedical Entity-Relation Extraction Method Using Machine Reading Comprehension.” Unpublished Master Thesis, Department of Information Management, National Taiwan University, Taipei, Taiwan. Yu, X., & Lam, W. (2010). Jointly Identifying Entities and Extracting Relations in Encyclopedia Text via A Graphical Model Approach. Coling 2010: Posters, 1399–1407. Yuan, Y., Zhou, X., Pan, S., Zhu, Q., Song, Z., & Guo, L. (2020). A Relation-Specific Attention Network for Joint Entity and Relation Extraction. 4, 4054–4060. Zelenko, D., Aone, C., & Richardella, A. (2003). Kernel Methods for Relation Extraction. Journal of Machine Learning Research, 3, 1083–1106. Zeng, D., Liu, K., Lai, S., Zhou, G., & Zhao, J. (2014). Relation Classification via Convolutional Deep Neural Network. Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, 2335–2344. Zeng, D., Zhang, R. H., & Liu, Q. (2020). CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning. ArXiv:1911.10438 [Cs]. Zeng, X., Zeng, D., He, S., Liu, K., & Zhao, J. (2018). Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 506–514. Zhang, Z., Shu, X., Yu, B., Liu, T., Zhao, J., Li, Q., & Guo, L. (2020). Distilling Knowledge from Well-Informed Soft Labels for Neural Relation Extraction. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 9620– 9627. Zheng, S., Wang, F., Bao, H., Hao, Y., Zhou, P., & Xu, B. (2017). Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme. ArXiv:1706.05075 [Cs]. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/84817 | - |
| dc.description.abstract | 實體關係萃取(Entity Relation Extraction, ERE)是自然語言處理(Natural Language Processing, NLP)領域中一項重要的任務，而其可以被拆分為實體萃取 (Entity Extraction, EE 及關係萃取(Relation Extraction, RE)兩個子任務，旨在抓取 句子中所有可以組成關係的實體。近年來，諸多實體關係萃取研究僅專注於一對 一的關係萃取，而忽略了一對多關係的實體關係萃取問題。 在本研究中，我們將透過聯合模型(joint model)將 ERE 視作實體標記任務， 藉以同時萃取出需要的實體以及關係。我們也使用了消融(ablation)實驗檢驗我們 在模型中所使用的組件是否提升我們所提出的模型之效能。實驗結果顯示，我們 提出的模型在一對一以及一對一加一對多的資料集中比基準模型還要好。最後我 們展示了幾個經過模型所產生的結果，藉此展示我們的模型的確比基準模型還要 有效。 | zh_TW |
| dc.description.abstract | Entity relation extraction is an important task in Natural Language Processing (NLP), which can be divided into Entity Extraction (EE) and Relation Extraction (RE), aiming to capture all types of relations triplets from unstructured texts. Most of prior studies on entity relation extraction concentrate on extracting one-to-one relations only; however, there do exist one-to-many relations in real world applications. In this research, we are going to treat entity-relation extraction as a sequence labeling problem using an end-to-end joint model to extract entities along with relations simultaneously. To see whether the components in our proposed model can improve the extraction effectiveness, we conduct ablation experiments in our evaluation. As the experiment result shows, our proposed model performs better than the state-of-the-art model on one-to-one and one-to-one + one-to-many datasets. Finally, we show some cases that illustrate the advantages of our model comparing to the benchmark model. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T22:27:11Z (GMT). No. of bitstreams: 1 U0001-2908202212513800.pdf: 2938646 bytes, checksum: 14cf0f2e92b8e69c3875cfa3090f16ba (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 口試委員審定書 i 致謝 ii 摘要 iii Abstract iv Table of Contents v List of Tables vii List of Figures viii Chapter 1 Introduction 1 1.1 Background ............................................................. 1 1.2 Research Motivation and Objective ...................................... 3 Chapter 2 Literature Review 7 2.1 Biomedical Entity and Relations......................................... 7 2.2 Entity and Relation Extraction ......................................... 8 2.3 Tagging-based Methods.................................................. 10 2.4 Summary................................................................ 13 Chapter 3 Methodology 14 3.1 Problem Formulation ................................................... 15 3.2 Our Tagging Scheme .................................................... 16 3.3 Multi-tower Model...................................................... 19 3.3.1 Bi-LSTM Layer in the Encoding Process ............................... 21 3.3.2 Tower as A Decoder for Each Relation Type ........................... 22 3.4 Our Extraction Rules................................................... 23 Chapter 4 Empirical Evaluation 26 4.1 Data Collection ....................................................... 26 4.2 Implementation Details ................................................ 28 4.3 Benchmark Technique ................................................... 29 4.4 Evaluation Metrics .................................................... 29 4.5 Experiment Results .................................................... 33 4.6 Ablation Study ........................................................ 35 4.7 Case Study ............................................................ 38 Chapter 5 Conclusion 42 5.1 Contributions ......................................................... 42 5.2 Limitations ........................................................... 42 5.3 Future Works .......................................................... 43 References 44 | |
| dc.language.iso | en | |
| dc.subject | 實體關係萃取 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 一對多關係 | zh_TW |
| dc.subject | 實體標記任務 | zh_TW |
| dc.subject | 一對一關係 | zh_TW |
| dc.subject | one-to-many relations | en |
| dc.subject | Entity relation extraction | en |
| dc.subject | Sequence labeling | en |
| dc.subject | Deep learning | en |
| dc.subject | one-to-one relations | en |
| dc.title | 運用基於深度學習的標注系統 針對一對一與一對多的實體關係萃取方法 | zh_TW |
| dc.title | Joint Biomedical Entity-Relation Extraction for 1-to-1 and 1-to-Many Relations Method Using a Deep-Learning-Based Sequence Labeling Method | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 楊錦生,吳家齊 | |
| dc.subject.keyword | 實體關係萃取,實體標記任務,深度學習,一對一關係,一對多關係, | zh_TW |
| dc.subject.keyword | Entity relation extraction,Sequence labeling,Deep learning,one-to-one relations,one-to-many relations, | en |
| dc.relation.page | 49 | |
| dc.identifier.doi | 10.6342/NTU202202922 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2022-08-30 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| dc.date.embargo-lift | 2022-08-30 | - |
| Appears in Collections: | 資訊管理學系 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| U0001-2908202212513800.pdf Access limited in NTU ip range | 2.87 MB | Adobe PDF |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.