MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective

• URL: http://arxiv.org/abs/2204.04391v1
• Date: Sat, 9 Apr 2022 05:18:20 GMT
• Title: MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective
• Authors: Xiao Wang, Shihan Dou, Limao Xiong, Yicheng Zou, Qi Zhang, Tao Gui, Liang Qiao, Zhanzhan Cheng, Xuanjing Huang
• Abstract summary: NER model has achieved promising performance on standard NER benchmarks. Recent studies show that previous approaches may over-rely on entity mention information, resulting in poor performance on out-of-vocabulary (OOV) entity recognition. We propose MINER, a novel NER learning framework, to remedy this issue from an information-theoretic perspective.
• Score: 57.19660234992812
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: NER model has achieved promising performance on standard NER benchmarks. However, recent studies show that previous approaches may over-rely on entity mention information, resulting in poor performance on out-of-vocabulary (OOV) entity recognition. In this work, we propose MINER, a novel NER learning framework, to remedy this issue from an information-theoretic perspective. The proposed approach contains two mutual information-based training objectives: i) generalizing information maximization, which enhances representation via deep understanding of context and entity surface forms; ii) superfluous information minimization, which discourages representation from rote memorizing entity names or exploiting biased cues in data. Experiments on various settings and datasets demonstrate that it achieves better performance in predicting OOV entities.

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