Zero-Resource Cross-Domain Named Entity Recognition

• URL: http://arxiv.org/abs/2002.05923v2
• Date: Tue, 19 May 2020 11:56:07 GMT
• Title: Zero-Resource Cross-Domain Named Entity Recognition
• Authors: Zihan Liu, Genta Indra Winata, Pascale Fung
• Abstract summary: Existing models for cross-domain named entity recognition rely on numerous unlabeled corpus or labeled NER training data in target domains. We propose a cross-domain NER model that does not use any external resources.
• Score: 68.83177074227598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing models for cross-domain named entity recognition (NER) rely on numerous unlabeled corpus or labeled NER training data in target domains. However, collecting data for low-resource target domains is not only expensive but also time-consuming. Hence, we propose a cross-domain NER model that does not use any external resources. We first introduce a Multi-Task Learning (MTL) by adding a new objective function to detect whether tokens are named entities or not. We then introduce a framework called Mixture of Entity Experts (MoEE) to improve the robustness for zero-resource domain adaptation. Finally, experimental results show that our model outperforms strong unsupervised cross-domain sequence labeling models, and the performance of our model is close to that of the state-of-the-art model which leverages extensive resources.

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