2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDHLT-13.4
Paper Title IMPROVING NER IN SOCIAL MEDIA VIA ENTITY TYPE-COMPATIBLE UNKNOWN WORD SUBSTITUTION
Authors Jian Xie, Kai Zhang, Lin Sun, Zhejiang University City College, China; Yindu Su, Zhejiang University, China; Chenxiang Xu, Zhejiang University City College, China
SessionHLT-13: Information Extraction
LocationGather.Town
Session Time:Thursday, 10 June, 14:00 - 14:45
Presentation Time:Thursday, 10 June, 14:00 - 14:45
Presentation Poster
Topic Human Language Technology: [HLT-STPA] Segmentation, Tagging, and Parsing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Named entity recognition (NER) is a fundamental task for information extraction (IE), and current state-of-the-art methods try to address this issue and achieve high performance on clean text (e.g., newswire genres). However, most of these algorithms do not generalize well when they transit to the noisy domain such as social media. To alleviate the noisy expression in social media data, we present a novel word substitution strategy based on constructing an entity type-compatible (ETC) semantic space. We substitute unknown words with the ETC words found by deep metric learning (DML) and nearest neighbor (NN) search. Comprehensive experiments show that the proposed framework achieves state-of-the-art performance on the W-NUT2017 dataset and the novel strategy brings good generality to multiple NER tools and previous work.