Abstract
Named entity recognition (NER) is a basic knowledge extraction task. At present, many domains face a lack of labeled data, but current models for low-resource NER does not utilize the features of domain text. In this paper, we propose the MAST-NER model to improve the NER performance on domain-specific text. This model introduces multiple type pools based on entity triggers, and enhances sequence tagging through a multi-head attention mechanism, where the query matrix is jointly constructed by each type of triggers. MAST-NER can take full advantages of entity triggers on domain text with similar sentence patterns, and enable each type of entity recognition to be enhanced. The experimental results show that the model in this paper can achieve higher cost-effectiveness, especially for domain datasets (up to 3.33%). For general domain datasets, this model also has a certain performance improvement.
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Acknowledgment
This paper is based on a research project supported by National Key Research and Development Project (Grant No. 2018YFB1703104) and National Natural Science Foundation of China (Grant No. 61671157).
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Xu, J., Li, M. (2022). MAST-NER: A Low-Resource Named Entity Recognition Method Based on Trigger Pool. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13370. Springer, Cham. https://doi.org/10.1007/978-3-031-10989-8_6
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