Explicitly Capturing Relations between Entity Mentions via Graph Neural Networks for Domain-specific Named Entity Recognition
Explicitly Capturing Relations between Entity Mentions via Graph Neural Networks for Domain-specific Named Entity Recognition
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DOI：
10.18653/v1/2021.acl-short.93
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Pei Chen;Haibo Ding;J. Araki;Ruihong Huang
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文献类型：
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作者：
Pei Chen;Haibo Ding;J. Araki;Ruihong Huang
命名实体识别（NER）在一般领域得到了深入研究，最近的系统在识别常见实体类型方面已经达到了人类水平的性能。然而，对于往往具有复杂上下文和行话实体类型的专业领域，NER 性能仍然中等。为了应对这些挑战，我们建议基于全局共指关系和局部依赖关系显式连接实体提及，以构建更好的实体提及表示。在我们的实验中，我们通过图神经网络合并了实体提及关系，并表明我们的系统显着提高了来自不同领域的两个数据集的 NER 性能。我们进一步表明，即使只有少量标记数据可用，所提出的轻量级系统也可以有效地将 NER 性能提升到更高水平，这对于特定领域的 NER 来说是理想的。
Named entity recognition (NER) is well studied for the general domain, and recent systems have achieved human-level performance for identifying common entity types. However, the NER performance is still moderate for specialized domains that tend to feature complicated contexts and jargonistic entity types. To address these challenges, we propose explicitly connecting entity mentions based on both global coreference relations and local dependency relations for building better entity mention representations. In our experiments, we incorporate entity mention relations by Graph Neural Networks and show that our system noticeably improves the NER performance on two datasets from different domains. We further show that the proposed lightweight system can effectively elevate the NER performance to a higher level even when only a tiny amount of labeled data is available, which is desirable for domain-specific NER.