Open AccessResearch on Chinese Event Extraction Method Based on HMM and Multi-stage Method
Author(s) -
Liying Zhan,
Xuping Jiang,
Qian Liu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1732/1/012024
Subject(s) - event (particle physics) , computer science , matching (statistics) , artificial intelligence , hidden markov model , recall rate , word (group theory) , extraction (chemistry) , stage (stratigraphy) , class (philosophy) , speech recognition , pattern recognition (psychology) , natural language processing , mathematics , statistics , paleontology , physics , geometry , chemistry , chromatography , quantum mechanics , biology
The existing Chinese event extraction methods are mostly one-stage methods. Even if there are two-stage methods, the correlation between the stages is not high, the extracted event trigger words and event elements have low matching degree, and there is a class imbalance problem in the training process. This paper proposes a Chinese event extraction method based on HMM and multi-stage method. The first stage of the method recognises the event trigger word positive examples in the text; the second stage classifies the trigger word positive examples recognised in the first stage, determines the event type, and forms the sequence of event trigger words; the third stage matches the event elements, and forms the sequence of event elements, according to the first two stages of event trigger word extraction results. This method effectively alleviates the class imbalance problem in training, improves the matching degree between event trigger words and event elements extraction, and obtains better extraction performance. The accuracy, recall rate and F value of Chinese event extraction are all More than 78%.