Progressive Joint Framework for Chinese Question Entity Discovery and Linking With Question Representations | |
Lin, Ziqi1,2,3; Zhang, Haidong1,2; Ni, Wancheng1,2; Yang, Yiping1 | |
发表期刊 | IEEE ACCESS |
ISSN | 2169-3536 |
2019 | |
卷号 | 7期号:-页码:146282-146300 |
摘要 | Chinese question entity discovery and linking (QEDL) may encounter short texts and small-scale annotated datasets, which may invalidate certain machine learning algorithms. In this paper, we propose a progressive joint framework for Chinese QEDL, which leverages the mutual dependency information of these two tasks to enhance the performance with each other. The framework uses the candidate entity generation (CEG) of entity linking to iteratively augment the overall process of entity discovery that consists of mention generation, filtering and merging modules. In mention generation module, to reduce the hand-crafted effort of the rule-based entity discovery, we develop a question representation method to generate domain-independent entity discovery rules, and use CEG to check the extracted mentions in priority order. This module can embed extracted mentions into other entity discovery methods as one feature or as extra mentions to alleviate insufficiencies of annotated datasets. The mentions filtering module leverages the joint features of extracted mentions and CEG's entities to build a voting model and filter out low-confidence mentions. Moreover, the mentions merging module merges different patterns' mention-entity pairs and check their corresponding candidate entities with CEG. During entity linking, we incorporate the joint features of questions, extracted mentions and CEG's entities into a ranking model for entity disambiguation. Finally, we conduct experiments on two real datasets and compare our approach with other state-of-the-art methods. The results illustrate that the proposed framework can reduce error accumulation and flexibly combine different entity discovery methods, which significantly improves the performance on small-scale datasets. |
关键词 | Entity discovery and linking information extraction joint method natural language processing question representation model |
DOI | 10.1109/ACCESS.2019.2944223 |
关键词[WOS] | STRATEGY |
收录类别 | SCI |
语种 | 英语 |
WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
WOS类目 | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications |
WOS记录号 | WOS:000498824000001 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 自然语言处理 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/29328 |
专题 | 综合信息系统研究中心_视知觉融合及其应用 |
通讯作者 | Ni, Wancheng |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Innovat Acad Artificial Intelligence, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Lin, Ziqi,Zhang, Haidong,Ni, Wancheng,et al. Progressive Joint Framework for Chinese Question Entity Discovery and Linking With Question Representations[J]. IEEE ACCESS,2019,7(-):146282-146300. |
APA | Lin, Ziqi,Zhang, Haidong,Ni, Wancheng,&Yang, Yiping.(2019).Progressive Joint Framework for Chinese Question Entity Discovery and Linking With Question Representations.IEEE ACCESS,7(-),146282-146300. |
MLA | Lin, Ziqi,et al."Progressive Joint Framework for Chinese Question Entity Discovery and Linking With Question Representations".IEEE ACCESS 7.-(2019):146282-146300. |
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