北京生物醫(yī)學(xué)工程

一種多特征融合的Web醫(yī)學(xué)信息語義關(guān)系抽取方法

An approach to relation extraction in the area of medical information on web based on multi-feature fusion

作者：龍麗英閆健卓方麗英李鵬英劉欣悅

單位：北京工業(yè)大學(xué)電子信息與控制工程學(xué)院(北京100124)

關(guān)鍵詞： Web醫(yī)學(xué)信息；語義關(guān)系抽取；多特征；混合句法分析；支持向量機(jī)

分類號：R318.04；TP391.04

出版年·卷·期（頁碼）：2016·35·3（243-248）

摘要：

目的為給用戶提供更為相關(guān)、整體和結(jié)構(gòu)化的Web醫(yī)學(xué)信息，提出一種多特征融合的語義關(guān)系抽取方法，以解決中文Web醫(yī)學(xué)信息中兩兩醫(yī)學(xué)實(shí)體之間語義關(guān)系的抽取。方法首先在混合句法分析算法的基礎(chǔ)上構(gòu)造包含詞項(xiàng)、語義、詞性、交互詞、實(shí)體對距離、實(shí)體類別以及最短依賴關(guān)系特征的特征向量并結(jié)合支持向量機(jī)實(shí)現(xiàn)。對Web醫(yī)學(xué)信息中師徒關(guān)系、擅長關(guān)系及從屬關(guān)系抽取實(shí)驗(yàn)，比較在不同句法分析下、不同特征作用及不同機(jī)器學(xué)習(xí)算法下的語義關(guān)系抽取效果。結(jié)果從F估計和算法運(yùn)行時間來看，混合句法分析下效果最佳。隨著特征的加入，抽取效果不斷提升，最后，對三類語義關(guān)系抽取最終獲得81.16%、95.94%和86.16%的F估計值。結(jié)論基于多特征融合的語義關(guān)系抽取方法對于Web醫(yī)學(xué)信息語義關(guān)系的抽取具有很好的效果。

Objective To provide more related,holistic and structured result for users by using the information extraction technology for the request of medical information on web.Methods This paper describes an approach to relation extraction in the area of medical information on web based on multi-feature fusion,in which the support vector machine algorithm combines with the feature vectors constructed by lexicon,semantics,part of speech,interactive,distance,entity type and shortest dependency relation path based on mixed parsing algorithm.This paper compares the results of relation extraction on different parsing,different features and different machine learning algorithm.Results From the view of F-measure and running time,the result of mixing parsing is perfect.By adding different feature,the results are promoted continually and finally the F-measure of the three relation extraction is 81.16%,95.94% and 86.16%,separately.Conclusions The approach to relation extraction in the area of medical information on web based on multi-feature fusion has a good performance.

參考文獻(xiàn)：

［1］Guo J, Xu G, Cheng X, et al. Named entity recognition in query［C］//Proceedings of the 32nd international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 2009: 267-274.

［2］Balog K, de Vries AP, Serdyukov P, et al. The first international workshop on entity-oriented search (EOS)［C］//ACM SIGIR Forum. ACM, 2012, 45(2): 43-50.

［3］Peng C, Gu J, Qian L. Research on Tree Kernel-Based Personal Relation Extraction［M］//Natural Language Processing and Chinese Computing. Springer Berlin Heidelberg, 2012: 225-236.

［4］Zhou GD, Zhang M. Extracting relation information from text documents by exploring various types of knowledge［J］. Information Processing & Management, 2007, 43(4): 969-982.

［5］Grave E. A convex relaxation for weakly supervised relation extraction［C］//Conference on Empirical Methods in Natural Language Processing (EMNLP). 2014.

［6］鄧擎, 樊孝忠, 楊立松. 用語義模式提取實(shí)體關(guān)系的方法［J］. 計算機(jī)工程, 2007, 33(10):212-214

Deng Qing, Fan Xiaozhong, Yang Lisong. Entity relation extraction method using semantic model［J］. Computer Engineering, 2007, 33(10): 212-214.

［7］Li W, Zhang P, Wei F, et al. A novel feature-based approach to Chinese entity relation extraction［C］//Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers. Association for Computational Linguistics, 2008: 89-92.

［8］Zelenko D, Aone C, Richardella A. Kernel methods for relation extraction［J］. The Journal of Machine Learning Research, 2003, 3: 1083-1106.

［9］Liu B, Qian L, Wang H, et al. Dependency-driven feature-based learning for extracting protein-protein interactions from biomedical text［C］//Proceedings of the 23rd International Conference on Computational Linguistics: Posters. Association for Computational Linguistics, 2010: 757-765.

［10］Miyao Y, Sagae K, Stre R, et al. Evaluating contributions of natural language parsers to protein-protein interaction extraction［J］. Bioinformatics, 2009, 25(3): 394-400.

［11］Kang N, Singh B, Bui C, et al. Knowledge-based extraction of adverse drug events from biomedical text［J］. BMC bioinformatics, 2014, 15(1): 64.

［12］Had M, Jungermann F, Morik K. Relation extraction for monitoring economic networks［M］//Natural Language Processing and Information Systems. Springer Berlin Heidelberg, 2010: 103-114.

［13］Miyao Y, Ohta T, Masuda K, et al. Semantic retrieval for the accurate identification of relational concepts in massive textbases［C］//Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2006: 1017-1024.

［14］Fei Wu, Weld DS. Open information extraction using wikipedia［C］// The Annual Meeting of the Association for Computational Linguistics (ACL-2010), 2010:118-127.

服務(wù)與反饋：

【文章下載】【加入收藏】

提示：您還未登錄，請登錄！點(diǎn)此登錄

51黑料吃瓜在线观看,51黑料官网|51黑料捷克街头搭讪_51黑料入口最新视频