聯合詞性與局部語境於中文健康照護實體關係擷取

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：33

、訪客IP：3.145.153.135

姓名

盧建寰(Chien-Huan Lu) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

聯合詞性與局部語境於中文健康照護實體關係擷取
(Joint Part-of-Speech and Local Context for Chinese Healthcare Entity-Relationship Extraction)

相關論文

★ 多重嵌入增強式門控圖序列神經網路之中文健康照護命名實體辨識	★ 基於腦電圖小波分析之中風病人癲癇偵測研究
★ 基於條件式生成對抗網路之資料擴增於思覺失調症自動判別	★ 標籤圖卷積增強式超圖注意力網路之中文健康照護文本多重分類
★ 運用合成器混合注意力改善BERT模型於科學語言編輯	★ 強化領域知識語言模型於中文醫療問題意圖分類
★ 管道式語言轉譯器之中文健康照護開放資訊擷取	★ 運用句嵌入向量重排序器增進中文醫療問答系統效能
★ 利用雙重註釋編碼器於中文健康照護實體連結	★ 運用異質圖注意力網路於中文醫療答案擷取式摘要
★ 學習使用者意圖於中文醫療問題生成式摘要	★ 標籤強化超圖注意力網路模型於精神疾病文本多標籤分類

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2027-8-24以後開放)

摘要(中)

開放式資訊擷取對於資訊理解甚是重要，此任務可以有效將複雜語句的文本簡單化，利用大量三元組形式來表達文本的重點信息，而所謂的三元組即是 (實體 1，關係，實體 2) ，利用這個形式來更加淺顯易懂的表達語句中潛在的關係。因此我們提出一個管道式模型 CHERE (Chinese Healthcare Entity-Relationship Extraction)，此模型將實體擷取模型和關係擷取模型分開訓練，再結合在一起使用。整體模型為利用 ME-MGNN 擷取出實體對，並採用基於轉換器 RoBERTa 自然語言預訓練模型的關係模型，從文本中擷取該實體對淺在的關係，進而形成三元組。由於現階段中文領域的開放式資訊擷取資料集相當缺乏，更遑論健康照護領域，因此我們人工標記一組語料庫，資料來源主要來自維基百科，總共有 5,879 個句子，包含 22,944 個實體以及 8,879 個三元組。其中此語料庫每句話都至少具有兩個以上的實體標記，部分資料具有三元組。最後透過實驗，我們得到 CHERE 在不同 F1-Score 評估指標下得到 0.6966 (Exact Match) 、0.7795 (Contain Match) 和 0.7986 (Token-Level Match) 等效能，其分數皆優異於其他開放式擷取模型 SpanOIE、Mulit2OIE、CHOIE。

摘要(英)

Open Information Extraction (OpenIE) is used to effectively simplify complex sentences in the form of triples such as (entity 1, relation, relation 2), which is an important technique to extract potential knowledge embedded in the sentences for natural language understanding. In this study, we propose a pipelined architecture, called CHERE (Chinese Healthcare Entity- Relationship Extraction), including an entity recognition model and a relationship extraction model. The ME-MGNN neural network is used to recognize entities, and their combined entity pairs are fed into the RoBERTa transformer for potential relation extraction. We manually annotate collected datasets for Chinese healthcare entity-relationship extraction because of a lack of publicly available benchmark data. The collected data mainly came from Wikipedia. In summary, our constructed data includes 5,879 sentences with a total of 22,944 entities and 8,879 triples. Each sentence contains at least two entities, where some entities contains the entity- relationship triples. Experimental results show our proposed CHERE model achieved the exact match F1 of 0.6966, contain match F1 of 0.7795, and token-level match F1 of 0.7986, which outperforms than compared models including SpanOIE, Multi2OIE, and CHOIE.

關鍵字(中)

★ 轉譯器
★ 開放式資訊擷取
★ 知識圖譜
★ 健康資訊學

關鍵字(英)

★ Transformers
★ Open Information Extraction
★ Knowledge Graph,
★ Health Informatics

論文目次

摘要 I
ABSTRACT II
致謝 III
目錄 IV
圖目錄 VI
表目錄 VII
第一章緒論 1
1-1 研究背景 1
1-2 動機與目的 3
1-3 章節概要 5
第二章相關研究 6
2-1 資訊擷取研究主題定義 6
2-2 開放式資訊擷取模型 8
2-2-1 語句分析和基於規則的方法 8
2-2-2 基於深度學習的模型 9
2-3 開放式資訊擷取資料集 12
第三章模型架構 15
3-1 系統架構 15
3-2 實體辨識模型 18
3-3 關係擷取模型 19
3-3-1 轉譯器編碼層 20
3-3-2 序列嵌入結合層 23
3-3-3 雙向長短期記憶神經網路層 27
3-3-4 全連結分類層 28
第四章實驗結果 29
4-1 資料集建置 29
4-2 效能指標 38
4-3 實驗設定 41
4-4 模型比較 43
4-5 消融實驗 45
4-6 嵌入向量分析 46
4-7 錯誤分析 47
第五章結論與未來工作 49
參考資料 50

參考文獻

[1] Alexander Yates, Michele Banko, Matthew Broadhead, Michael Cafarella, Oren Etzioni, and Stephen Soderland. 2007. TextRunner: Open Information Extraction on the Web. In Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT). Association for Computational Linguistics, pages 25–26.
[2] Janara Christensen, Mausam, Stephen Soderland, and Oren Etzioni. 2010. Semantic Role Labeling for Open Information Extraction. In Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading. Association for Computational Linguistics, pages 52–60.
[3] Harinder Pal and Mausam. 2016. Demonyms and Compound Relational Nouns in Nominal Open IE. In Proceedings of the 5th Workshop on Automated Knowledge Base Constructio. Association for Computational Linguistics, pages 35-39. 10.18653/v1/W16-1307
[4] Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
[5] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin, Attention Is All You Need. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017. page 5998--6008.
[6] Pennington, J., R. Socher, and C. Manning. 2014. GloVe: Global Vectors for Word Representation. in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pages 1532–1543. 10.3115/v1/D14-1162
[7] Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. 2019.BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Association for Computational Linguistics, pages 4171–4186. 10.18653/v1/N19-1423.
[8] Liu Zhuang, Lin Wayne, Shi Ya, and Zhao Jun. 2021. A Robustly Optimized BERT Pre-training Approach with Post-training. In Proceedings of the 20th Chinese National Conference on Computational Linguistics, pages 1218–1227.
[9] Lung-Hao Lee and Yi Lu. 2021. Multiple Embeddings Enhanced Multi-Graph Neural Networks for Chinese Healthcare Named Entity Recognition. IEEE Journal of Biomedical and Health Informatics, pages 2801-2810.
[10] Fei Wu and Daniel S. Weld. 2010. Open Information Extraction Using Wikipedia. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, pages 118–127.
[11] Mausam, Michael Schmitz, Stephen Soderland, Robert Bart, and Oren Etzioni. 2012. Open Language Learning for Information Extraction. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Association for Computational Linguistics, pages 523–534.
[12] Qiu, L. and Y. Zhang. ZORE: A Syntax-based System for Chinese Open Relation Extraction. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 2014. Association for Computational Linguistics, pages 1870–1880.
[13] Jia, S., et al., Chinese Open Relation Extraction and Knowledge Base Establishment. ACM Trans. ACM Transactions on Asian and Low-Resource Language Information Processing. 2018, pages 1–22.
[14] Gabor Angeli, Melvin Jose Johnson Premkumar, and Christopher D. Manning. 2015. Leveraging Linguistic Structure For Open Domain Information Extraction. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, pages 344–354. 10.3115/v1/P15-1034

[15] Gabriel Stanovsky, Julian Michael, Luke Zettlemoyer and Ido Dagan. Supervised Open Information Extraction. 2018. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). Association for Computational Linguistics, pages 344–354.
[16] Arpita Roy, Youngja Park, Taesung Lee, and Shimei Pan. 2019. Supervising Unsupervised Open Information Extraction Models. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing . Association for Computational Linguistics, Association for Computational Linguistics, page 728–737. 10.18653/v1/D19-1067.

[17] Junlang Zhan and Hai Zhao. 2020. Span Model for Open Information Extraction on Accurate Corpus. In Proceedings of the AAAI Conference on Artificial Intelligence, 34(05) : 9523-9530.
[18] Ro, Y., Y. Lee, and P. Kang. 2020. Multi^2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT. in Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pages 1107–1117. 10.18653/v1/2020.findings-emnlp.99.

[19] 鄭少鈞。2022年1月。管道式語言轉譯器之中文健康照護開放資訊擷取。國立中央大學電機工程學系碩士論文。
[20] Fader, A., S. Soderland, and O. 2011. Etzioni. Identifying Relations for Open Information Extraction. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, page 1535–1545.
[21] Gashteovski, K., et al. 2019. OPIEC: An Open Information Extraction Corpus. CoRR, 2019. In Proceedings of the Conference of Automatic Knowledge Base Construction (AKBC) 2019.
[22] Nakashole, N., G. Weikum, and F. Suchanek. 2012. PATTY: A Taxonomy of Relational Patterns with Semantic Types. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Association for Computational Linguistics, pages 1135–1145.
[23] Stanovsky, G. and I. Dagan. 2016. Creating a Large Benchmark for Open Information Extraction. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pages 2300–2305.
[24] Rudolf Schneider, Tom Oberhauser, Tobias Klatt, Felix A. Gers, and Alexander Löser. 2017. Analysing Errors of Open Information Extraction Systems. In Proceedings of the First Workshop on Building Linguistically Generalizable NLP Systems. Association for Computational Linguistics, pages 11–18. 10.18653/v1/W17-5402.

[25] Bhardwaj, S., S. Aggarwal, and M. Mausam. 2019. CaRB: A Crowdsourced Benchmark for Open IE. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing . Association for Computational Linguistics,pages 6262–6267.
[26] Lechelle, W., F. Gotti, and P. Langlais. 2019. WiRe57 : A Fine-Grained Benchmark for Open Information Extraction. In Proceedings of the 13th Linguistic Annotation Workshop. Association for Computational Linguistics, pages 6–15.
[27] Gashteovski, K., R. Gemulla, and L. del Corro. 2017. MinIE: Minimizing Facts in Open Information Extraction. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pages 2630–2640. 10.18653/v1/D17-1278.

[28] Yuen-Hsien Tseng, Lung-Hao Lee, Shu-Yen Lin, Bo-Shun Liao, Mei-Jun Liu, Hsin-Hsi Chen, Oren Etzioni and Anthony Fader. 2014. Chinese Open Relation Extraction for Knowledge Acquisition. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers. Association for Computational Linguistics, pages 12–16. 10.3115/v1/E14-4003.

[29] Mikolov, T., Chen, K., Corrado, G., Dean, J. . 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
[30] Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019. Albert: A lite bert for self-supervised learning of language representations. In Proceeding of the International Conference on Learning Representations.
[31] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI Blog, 1(8):9.
[32] Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, and Guoping Hu. 2020. Revisiting Pre-Trained Models for Chinese Natural Language Processing. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 657–668.

指導教授

李龍豪(Lung-Hao Lee)

審核日期

2022-8-25

推文