Research on Chinese Named Entity Recognition in the Marine Field

Abstract

Named entity recognition is a basic problem in natural language processing, and also an indispensable part of many natural language processing technologies such as information extraction and information retrieval. There are a large number of proprietary entities in the marine field. In this paper, we propose a method based on user dictionary and conditional random field, which recognize energy, organisms and drugs named entities in the marine filed. Analyzing the characteristics of marine named entities and successively adding different features to raise the recognition rate. Ultimately, the accuracy rate reaches over 96.95% and the recalling rate reaches over 96.41%. In addition, the method of combining neural network with statistical model (BiLSTM-CRF) is also used in this paper. In order to reduce the influence of inaccurate word segmentation on named entity recognition in the marine field, we use character vectors instead of word vectors as input of neural network layer. Finally, the accuracy rate reaches over 72.23% and the recalling rate reaches over 66.76%. The experimental results confirm that the method based on user dictionary and conditional random field is feasible for named entity recognition in the marine field.