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    <abstract>A bottleneck problem with Chinese named entity recognition (NER) in new domains is the lack of annotated data. One solution is to utilize the method of distant supervision, which has been widely used in relation extraction, to automatically populate annotated training data without humancost. The distant supervision assumption here is that if a string in text is included in a predefined dictionary of entities, the string might be an entity. However, this kind of auto-generated data suffers from two main problems: incomplete and noisy annotations, which affect the performance of NER models. In this paper, we propose a novel approach which can partially solve the above problems of distant supervision for NER. In our approach, to handle the incomplete problem, we apply partial annotation learning to reduce the effect of unknown labels of characters. As for noisy annotation, we design an instance selector based on reinforcement learning to distinguish positive sentences from auto-generated annotations. In experiments, we create two datasets for Chinese named entity recognition in two domains with the help of distant supervision. The experimental results show that the proposed approach obtains better performance than the comparison systems on both two datasets.</abstract>
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%0 Conference Proceedings
%T Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning
%A Yang, Yaosheng
%A Chen, Wenliang
%A Li, Zhenghua
%A He, Zhengqiu
%A Zhang, Min
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F yang-etal-2018-distantly
%X A bottleneck problem with Chinese named entity recognition (NER) in new domains is the lack of annotated data. One solution is to utilize the method of distant supervision, which has been widely used in relation extraction, to automatically populate annotated training data without humancost. The distant supervision assumption here is that if a string in text is included in a predefined dictionary of entities, the string might be an entity. However, this kind of auto-generated data suffers from two main problems: incomplete and noisy annotations, which affect the performance of NER models. In this paper, we propose a novel approach which can partially solve the above problems of distant supervision for NER. In our approach, to handle the incomplete problem, we apply partial annotation learning to reduce the effect of unknown labels of characters. As for noisy annotation, we design an instance selector based on reinforcement learning to distinguish positive sentences from auto-generated annotations. In experiments, we create two datasets for Chinese named entity recognition in two domains with the help of distant supervision. The experimental results show that the proposed approach obtains better performance than the comparison systems on both two datasets.
%U https://aclanthology.org/C18-1183
%P 2159-2169

Markdown (Informal)

[Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning](https://aclanthology.org/C18-1183) (Yang et al., COLING 2018)

• Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning (Yang et al., COLING 2018)

ACL

• Yaosheng Yang, Wenliang Chen, Zhenghua Li, Zhengqiu He, and Min Zhang. 2018. Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2159–2169, Santa Fe, New Mexico, USA. Association for Computational Linguistics.