Enhancing Entity Boundary Detection for Better C hinese Named Entity Recognition (2021)

Abstract: In comparison with English, due to the lack of explicit word boundary and tenses information, Chinese Named Entity Recognition (NER) is much more challenging. In this paper, we propose a boundary enhanced approach for better Chinese NER. In particular, our approach enhances the boundary information from two perspectives. On one hand, we enhance the representation of the internal dependency of phrases by an additional Graph Attention Network(GAT) layer. On the other hand, taking the entity head-tail prediction (i.e., boundaries) as an auxiliary task, we propose an unified framework to learn the boundary information and recognize the NE jointly. Experiments on both the OntoNotes and the Weibo corpora show the effectiveness of our approach.

## introduction given a sentence, the ner task aims to identify the noun phrases having special meanings that predefined. due to its importance on many downstream tasks, such as relation extraction @xcite , coreference resolution @xcite and knowledge graphs @xcite , ner has attracted much attention for long time. in comparison with english, due to the lack of explicit word boundary and tenses information, chinese ner is much more challenging. in fact, the performance of the current sotas in chinese is far inferior to that in english, the gap is about 10% in f1-measure. in this paper, we propose a boundary enhancing approach for better chinese ner. firstly, using star-transformer @xcite , we construct a lightweight baseline system. benefit from the unique star topological structure, star-transformer is more dominant in representing long distance sequence, and thus, our baseline achieves comparable performance to the sotas. considering the deficiency in the representation of local sequence information, we then try to enhance the local boundary information. in particular, our approach enhances the boundary information from two perspectives. on one hand, we add an additional gat @xcite layer to capture the internal dependency of phrases. in this way, boundaries can be distinguished implicitly, while the semantic information within the phrase is enhanced. on the other hand, we add an auxiliary task to predict the head and tail of entities. in this way, using the framework of multi-tasking learning, we can learn the boundary information explicitly and help the ner task. experiments show the effectiveness of our approach. it should be noted that, our approach obtains the new state-of-the-art results on both the ontonotes and the weibo corpora. that means our approach can perform well for both written and non-written texts. ## related work as is well known, most researches cast the ner task as a traditional sequence labelling problem, and many models extending the bi-lstm+crf architecture are proposed @xcite @xcite @xcite . although the attention-based model, i.e., transformer @xcite , has gradually surpassed the traditional rnn model @xcite in various fields, @xcite has verified that the fully connected transformer mechanism does not work well on ner. until recently, some researches show that star-transformer can work well on ner owing to its lightweight topological structure @xcite . moreover, lexical and dependent information has been widely used in this task @xcite @xcite @xcite to better capture local semantic information. ## model we also treat ner as a sequence labeling task, decoding with a classical crf @xcite . figure 1 shows the complete model. we can find that the encoder of our model consists of three parts, i.e., gru-based head and tail representation layer, star-transformer based contextual embedding layer, and gat-based dependency embedding layer. ## conclusion in this paper, we mainly focus on the impact of boundary information on chinese ner. we firstly propose a star-transformer based ner system. then both explicit head and tail boundary information and dependency gat-based implicit boundary information are combined to improve chinese ner. experiments on both the ontonotes and the weibo corpora show the effectiveness of our approach.