ELCA: Enhanced boundary location for Chinese named entity recognition via contextual association

Named Entity Recognition (NER) is a fundamental task that aids in the completion of other tasks such as text understanding, information retrieval and question answering in Natural Language Processing (NLP). In recent years, the use of a mix of character-word structure and dictionary information for Chinese NER has been demonstrated to be effective. As a representative of hybrid models, Lattice-LSTM has obtained better benchmarking results in several publicly available Chinese NER datasets. However, Lattice-LSTM does not address the issue of long-distance entities or the detection of several entities with the same character. At the same time, the ambiguity of entity boundary information also leads to a decrease in the accuracy of embedding NER. This paper proposes ELCA: Enhanced Boundary Location for Chinese Named Entity Recognition Via Contextual Association, a method that solves the problem of long-distance dependent entities by using sentence-level position information. At the same time, it uses adaptive word convolution to overcome the problem of several entities sharing the same character. ELCA achieves the state-of-the-art outcomes in Chinese Word Segmentation and Chinese NER.