Year: 2022
Type: conference-paper
Abstract: Event coreference resolution (ECR) aims to cluster event mentions that refer to the same real-world events.Deep learning methods have achieved SOTA results on the ECR task.However, due to the encoding length limitation, previous methods either adopt classical pairwise models based on sentence-level context or split each document into multiple chunks and encode them separately.They failed to capture the interactions and contextual cues among those long-distance event mentions.Besides, highlevel information, such as event topics, is rarely considered to enhance representation learning for ECR.To address the above two issues, we first apply a Longformer-based encoder to obtain the document-level embeddings and an encoder with a trigger-mask mechanism to learn sentence-level embeddings based on local context.In addition, we propose an event topic generator to infer the latent topic-level representations.Finally, using the above event embeddings, we employ a multiple tensor matching method to capture their interactions at the document, sentence, and topic levels.Experimental results on the KBP 2017 dataset show that our model 1 outperforms the SOTA baselines.
Authors: Sheng Xu, Peifeng Li, Qiaoming Zhu
Institution: Soochow University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4385573735