Named Entity Recognition Based on Character-level Language Models and Attention Mechanism

Abstract

As a basic task in the field of natural language processing, named entity recognition plays an important role in text data processing tasks. Extracting features from the original text can be considered as the first step in the identification of named entities, but on this basic issue, traditional research still stays at the coarser granularity of words. Unlike traditional research, this paper focuses on finer granularity-character-level named entity recognition research. In order to fully extract the character-level feature representation from the character-level language model, this paper uses CNN and BiLSTM to perform feature extraction together, and introduces the attention mechanism to achieve more effective combination of character features and word features, then combines with BiLSTM-CRF to construct a complete end-to-end deep learning model (At- BiLSTM-CNNs-CRF). The experimental results show that its recognition ability exceeds most deep learning models.