Capturing semantic features to improve Chinese event detection. Issue 2 (31st August 2021)

• Record Type: Journal Article
• Authors: Ma, Xiaobo; Liu, Yongbin; Ouyang, Chunping
• Is Part Of: CAAI transactions on intelligence technology. Volume 7:Issue 2(2022)
• Page Start: 219
• Page End: 227
• Publication Date: 2021-08-31
• Subjects: dependency parser -- event detection -- hybrid representation learning -- semantic feature; grammars -- text analysis -- natural language processing -- information retrieval; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/cit2.12062
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract: 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.