Chinese Sentence-level Event Factuality Identification with Recursive Neural Network

Sentence-level event factuality identification (SEFI) aims to identify the factuality of an event presented in a sentence. Recent neural network-based approaches have demonstrated the efficacy of the shortest dependency path, but these methods lack semantic information compared with continuous text...

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Bibliographic Details
Main Authors: Yi, Qingqing, Qian, Zhong, Li, Peifeng, Zhu, Qiaoming
Format: Conference Proceeding
Language:English
Subjects:
dependency tree
event factuality identification
recursive neural network
Online Access:Request full text
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Summary:Sentence-level event factuality identification (SEFI) aims to identify the factuality of an event presented in a sentence. Recent neural network-based approaches have demonstrated the efficacy of the shortest dependency path, but these methods lack semantic information compared with continuous text fragments and may lead to the omission of useful information. In addition, dependency paths are relatively flat. So far, most previous work focused on English datasets, and neglected Chinese tasks. And the existing Chinese SEFI methods ignore the syntactic information. To overcome the above issues, we propose a Chinese event factuality identification model based on dependency trees. We adopt a recursive neural network-based module that fuses event selected predicates, degree words, negative words, and event triggers to capture long-range relations among them. Experimental results on the Chinese event factuality corpus show that our proposed method outperforms other baselines.
ISSN:2161-4407
DOI:10.1109/IJCNN55064.2022.9892209