AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction

• URL: http://arxiv.org/abs/2109.04726v3
• Date: Thu, 18 May 2023 06:04:10 GMT
• Title: AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction
• Authors: Dong-Ho Lee, Ravi Kiran Selvam, Sheikh Muhammad Sarwar, Bill Yuchen Lin, Fred Morstatter, Jay Pujara, Elizabeth Boschee, James Allan and Xiang Ren
• Abstract summary: We present a novel framework to improve NER performance by automatically generating and leveraging entity triggers'' Our framework leverages post-hoc explanation to generate rationales and strengthens a model's prior knowledge using an embedding technique. AutoTriggER shows strong label-efficiency, is capable of generalizing to unseen entities, and outperforms the RoBERTa-CRF baseline by nearly 0.5 F1 points on average.
• Score: 54.20039200180071
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural models for named entity recognition (NER) have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervision and auxiliary information such as explanations. However, the costs of acquiring such additional information are generally prohibitive. In this paper, we present a novel two-stage framework (AutoTriggER) to improve NER performance by automatically generating and leveraging ``entity triggers'' which are human-readable cues in the text that help guide the model to make better decisions. Our framework leverages post-hoc explanation to generate rationales and strengthens a model's prior knowledge using an embedding interpolation technique. This approach allows models to exploit triggers to infer entity boundaries and types instead of solely memorizing the entity words themselves. Through experiments on three well-studied NER datasets, AutoTriggER shows strong label-efficiency, is capable of generalizing to unseen entities, and outperforms the RoBERTa-CRF baseline by nearly 0.5 F1 points on average.

Related papers

• Leveraging large language models for efficient representation learning for entity resolution [2.035773073690598]
  TriBERTa is a supervised entity resolution system that learns representations for entity matching. The proposed approach outperforms state-of-the-art representations by a margin of 3 - 19%.
  arXiv  Detail & Related papers  (2024-11-15T23:24:07Z)
• In-Context Learning for Few-Shot Nested Named Entity Recognition [53.55310639969833]
  We introduce an effective and innovative ICL framework for the setting of few-shot nested NER. We improve the ICL prompt by devising a novel example demonstration selection mechanism, EnDe retriever. In EnDe retriever, we employ contrastive learning to perform three types of representation learning, in terms of semantic similarity, boundary similarity, and label similarity.
  arXiv  Detail & Related papers  (2024-02-02T06:57:53Z)
• A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition [74.79785063365289]
  Existing models for named entity recognition (NER) are mainly based on large-scale labeled datasets. We propose a Confidence-based Partial Label Learning (CPLL) method to integrate the prior confidence (given by annotators) and posterior confidences (learned by models) for crowd-annotated NER.
  arXiv  Detail & Related papers  (2023-05-21T15:31:23Z)
• Gaussian Prior Reinforcement Learning for Nested Named Entity Recognition [52.46740830977898]
  We propose a novel seq2seq model named GPRL, which formulates the nested NER task as an entity triplet sequence generation process. Experiments on three nested NER datasets demonstrate that GPRL outperforms previous nested NER models.
  arXiv  Detail & Related papers  (2023-05-12T05:55:34Z)
• Multi-task Transformer with Relation-attention and Type-attention for Named Entity Recognition [35.44123819012004]
  Named entity recognition (NER) is an important research problem in natural language processing. This paper proposes a multi-task Transformer, which incorporates an entity boundary detection task into the named entity recognition task.
  arXiv  Detail & Related papers  (2023-03-20T05:11:22Z)
• Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training [66.80558875393565]
  We study the problem of training named entity recognition (NER) models using only distantly-labeled data. We propose a noise-robust learning scheme comprised of a new loss function and a noisy label removal step. Our method achieves superior performance, outperforming existing distantly-supervised NER models by significant margins.
  arXiv  Detail & Related papers  (2021-09-10T17:19:56Z)
• BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision [49.42215511723874]
  We propose a new computational framework -- BOND -- to improve the prediction performance of NER models. Specifically, we propose a two-stage training algorithm: In the first stage, we adapt the pre-trained language model to the NER tasks using the distant labels. In the second stage, we drop the distant labels, and propose a self-training approach to further improve the model performance.
  arXiv  Detail & Related papers  (2020-06-28T04:55:39Z)
• Named Entity Recognition without Labelled Data: A Weak Supervision Approach [23.05371427663683]
  This paper presents a simple but powerful approach to learn NER models in the absence of labelled data through weak supervision. The approach relies on a broad spectrum of labelling functions to automatically annotate texts from the target domain. A sequence labelling model can finally be trained on the basis of this unified annotation.
  arXiv  Detail & Related papers  (2020-04-30T12:29:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.