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Low-Resource Named Entity Recognition Based on Multi-hop Dependency Trigger

arXiv 2109.07118 ↗First posted 15 Sep 2021cs.CL cs.AIcited by 0

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Abstract (arXiv)

This paper presents a simple and effective approach in low-resource named entity recognition (NER) based on multi-hop dependency trigger. Dependency trigger refer to salient nodes relative to a entity in the dependency graph of a context sentence. Our main observation is that there often exists trigger which play an important role to recognize the location and type of entity in sentence. Previous research has used manual labelling of trigger. Our main contribution is to propose use a syntactic parser to automatically annotate trigger. Experiments on two English datasets (CONLL 2003 and BC5CDR) show that the proposed method is comparable to the previous trigger-based NER model.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 17 references that are in the map. First posted on arXiv 15 Sep 2021.

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References · 11

  1. 2017Prototypical Networks for Few-shot Learningcited by 3,001
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  4. 2016End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRFcited by 341
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  6. 2020SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teachingcited by 71
  7. 2020Few-Shot Named Entity Recognition: A Comprehensive Studycited by 41
  8. 2020A Frustratingly Easy Approach for Entity and Relation Extractioncited by 37
  9. 2020TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognitioncited by 30
  10. 2017Neural Models for Sequence Chunkingcited by 10
  11. 2017Improving Slot Filling Performance with Attentive Neural Networks on Dependency Structurescited by 7

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