Open AccessA BOW-Based Sentence Embedding Method for Chinese Event Identification
Author(s) -
Lizheng Gao,
Gang Zhou,
Jiancheng Luo
Publication year - 2020
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 - 10.1088/1742-6596/1486/4/042012
Subject(s) - computer science , event (particle physics) , sentence , identification (biology) , natural language processing , embedding , artificial intelligence , word embedding , word (group theory) , scalability , simple (philosophy) , mathematics , philosophy , botany , physics , geometry , epistemology , quantum mechanics , database , biology
Event extraction is an important branch of information extraction. It involves two challenging issues: event identification and argument identification. Most approaches of event identification are trigger-based, which suffer a lot from sample imbalance, word ambiguity, low scalability, etc. To solve these problems, we attempt to explore sentence-based event identification, whose main issue is sentence representation. With the success of word embedding, it has attracted much attention to generate semantic embeddings of sentences. In this paper, we propose a simple BOW-based sentence embedding method to represent event sentences for Chinese event identification. We compute embeddings on the dependency tree and map different relations to simple arithmetic operations. We evaluate our method with a dataset of Chinese event identification and compare the result with other BOW-based methods. The results show that our approach significantly outperforms other BOW-based methods.