Entity extraction based on the parts of speech attention mechanism

Abstract

Entity extraction is an information extraction technique that aims to locate and classify named entities (e.g., organizations, locations, persons...), which is a very important and fundamental problem in natural language processing. On the research of entity extraction, numerous models ignore the learning of grammatical structure. Considering the shortcomings of previous models, this paper first proposes the PALC (POStag-Attention-LSTM-CRF) model, which adds POS (part of speech) features to entity extraction. Specially, PALC fuses POS features with other features through a multi-layer bidirectional LSTM network and attention mechanism to improve the effect of entity extraction. The experimental results show that the accuracy of the PALC model in this paper on the CONLL03 dataset can be 90.65%, on the CONLL03 dataset can be 84.86%, and on OntoNote 5.0 English dataset can be 86.99%.