Capturing semantic features to improve Chinese event detection

CAAI Transactions on Intelligence Technology

article

Xiaobo Ma¹  Yongbin Liu¹  Chunping Ouyang¹ 

[1] School of Computing, University of South China;Hunan provincial base for scientific and technological innovation cooperation

关键词: dependency parser;    event detection;    hybrid representation learning;    semantic feature;    grammars;    text analysis;    natural language processing;    information retrieval;   

DOI  :  10.1049/cit2.12062

学科分类：数学（综合）

来源: Wiley

【 摘 要 】

Current Chinese event detection methods commonly use word embedding to capture semantic representation, but these methods find it difficult to capture the dependence relationship between the trigger words and other words in the same sentence. Based on the simple evaluation, it is known that a dependency parser can effectively capture dependency relationships and improve the accuracy of event categorisation. This study proposes a novel architecture that models a hybrid representation to summarise semantic and structural information from both characters and words. This model can capture rich semantic features for the event detection task by incorporating the semantic representation generated from the dependency parser. The authors evaluate different models on kbp 2017 corpus. The experimental results show that the proposed method can significantly improve performance in Chinese event detection.

【 授权许可】

CC BY|CC BY-ND|CC BY-NC|CC BY-NC-ND