Chinese NER Using Dynamic Meta-Embeddings

Abstract

Named entity recognition (NER) is one of the basic techniques in natural language processing tasks. Chinese NER is complicated and difficult which remains a major challenge. One of the main reasons is that the boundaries of entities in Chinese are blurred and closely related to word segmentation results. Previous studies for this task have broadly divided into two categories, word-based, and character-based methods. However, the former class suffers from the word segmentation errors, and the latter cannot make full use of the information on multiple granularities. To address these problems, we investigate a new dynamic meta-embeddings method and apply it to Chinese NER task, which utilizes attention mechanism to combine features of both character and word granularity in embedding layer. The meta-embeddings created by our method are dynamic, data-specific, and task-specific, since the meta-embeddings for same characters in different sentence sequences are distinct. The experiments on MSRA and LiteratureNER datasets validate the effectiveness of our model, and this method achieves the state-of-the-art results on LiteratureNER.