NeurSEG: A Segment Driven Deep Neural Model for Nested Named Entity Recognition
Author:
Affiliation:
1. Nanyang Technological University, Singapore, Singapore
2. Baidu Inc., Beijing, China
Funder
RIE2020 Industry Alignment Fund Industry Collaboration Projects (IAF-ICP) Funding Initiative
Singapore Telecommunications Limited Singtel, through Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU)
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3607827.3616837
Reference44 articles.
1. Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution
2. SciBERT: A Pretrained Language Model for Scientific Text
3. Darina Benikova Chris Biemann and Marc Reznicek. 2014. NoSta-D Named Entity Annotation for German: Guidelines and Dataset.. In LREC. 2524--2531. Darina Benikova Chris Biemann and Marc Reznicek. 2014. NoSta-D Named Entity Annotation for German: Guidelines and Dataset.. In LREC. 2524--2531.
4. Stacked ensemble combined with fuzzy matching for biomedical named entity recognition of diseases
5. Enriching Word Vectors with Subword Information
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