Research on Event Extraction Model Based on Semantic Features of Chinese Words

Event Extraction (EE) is an important task in Natural Language Understanding (NLU). As the complexity of Chinese structure, Chinese EE is more difficult than English EE. According to the characteristics of Chinese, this paper designed a Semantic-GRU (Sem-GRU) model, which integrates Chinese word context semantics, Chinese word glyph semantics and Chinese word structure semantics. And this paper uses the model for Chinese Event Trigger Extraction (ETE) task. The experiment is compared in two tasks: ETE and Named Entity Recognition (NER). In ETE, the paper uses ACE 2005 Chinese event dataset to compare the existing research, the effect reaches 75.8 %. In NER, the paper uses MSRA dataset, which reaches 90.3 %, better than other models

A Deep Learning Entity Extraction Model for Chinese Government Documents

In this paper, we propose a combined Whole-Word-Masking based Robustly Optimized BERT pretraining approach with dictionary embedding entities recognition model for Chinese documents. By using multiple feature vectors generated by such as Roberta and domain dictionaries as embedding layers, the contextual semantic information of the text is fully considered. Meanwhile, Bi-directional Long Short-Term Memory(BiLSTM) and a multi-head attention mechanism are used to learn the information of long-distance dependency of the text. We use conditional random field(CRF) to obtain the global optimal annotation sequence, which is expected to improve the performance of the model. In this paper, we conduct comparison experiments with five baseline-based methods in the official document dataset of government affairs domain. The Precision of the model is 91.8%, Recall is 90.5%, and F1 value is 91.1%, which are better than other baseline models, indicating that the proposed model is more accurate for recognizing named entities in government documents