| 依存句法特征的科研命名实体识别算法 |
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| 引用本文： | 赵华茗,钱力,余丽.依存句法特征的科研命名实体识别算法[J].图书情报工作,2020,64(11):108-115. |
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| 作者姓名： | 赵华茗 钱力 余丽 |
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| 作者单位： | 1.中国科学院文献情报中心 北京 100190;2.中国科学院大学经济与管理学院图书情报与档案管理系 北京 100190 |
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| 基金项目： | 本文系中国科学院文献情报能力建设专项项目"文献情报'数据湖’及开放式大数据框架建设"（项目编号：院1852）与国家科技图书文献中心专项任务"多源数据增值与知识计算方法研究"（项目编号：K180201001）研究成果之一。 |
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| 摘 要： | 目的/意义] 探索科研命名实体及其关系的识别与抽取,提升其在长句等复杂情况下的识别效果,为进一步的应用提供参考与借鉴。方法/过程] 以依存句法特征分析为基础,提出一种科研命名实体关系抽取方法,过程包括：①使用Standford Tagger工具对目标文本进行词性标注;②基于标注结果,围绕核心谓词和SAO结构,将目标文本分割为结构规范的语义片段;③通过依存句法分析,找出与核心谓词语义相关的主语和宾语,构成（实体,关系,实体）三元组。结果/结论] 与Ollie、Reverb等主流算法进行的对比测试表明,该方法可以有效提升科研命名实体识别的准确性。
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| 关 键 词： | 依存句法分析 科研命名实体 实体识别 关系抽取 |
| 收稿时间： | 2019-09-24 |
| 修稿时间： | 2020-02-02 |
A Research Entity Recognition Algorithm Based on Dependency Parsing |
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| Zhao Huaming,Qian Li,Yu Li.A Research Entity Recognition Algorithm Based on Dependency Parsing[J].Library and Information Service,2020,64(11):108-115. |
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| Authors: | Zhao Huaming Qian Li Yu Li |
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| Institution: | 1.National Science Library, Chinese Academy of Sciences, Beijing 100190;2.Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190 |
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| Abstract: | Purpose/significance] To explore the recognition and extraction of research entities and their relationships, improve their recognition effect in complex situations such as long sentences, and provide reference for further application. Method/process] Based on the analysis of dependency syntactic features, a method for recognizing and extracting research entity relations was proposed, which includes:POS tagging of the target text using Standford Tagger tool; based on annotation results, the target text was divided into semantic segments of structure specification around the core predicate and SAO structure; through dependency parsing, we can find out the subject and object related to the core predicate and form a triple of entities, relationships and entities. Result/conclusion] This method is compared with Ollie and Reverb mainstream algorithm. Experiments show that this method can effectively improve the accuracy of scientific entity recognition. |
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| Keywords: | dependency parsing research entity entity recognition relation extraction |
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