CoRec: An Easy Approach for Coordination Recognition
Qing Wang Haojie Jia Wenfei Song Qi Li
In this paper, we observe and address the challenges of the coordination
recognition task. Most existing methods rely on syntactic parsers to identify
the coordinators in a sentence and detect the coordination boundaries. However,
state-of-the-art syntactic parsers are slow and suffer from errors, especially
for long and complicated sentences. To better solve the problems, we propose a
pipeline model COordination RECognizer (CoRec). It consists of two components:
coordinator identifier and conjunct boundary detector. The experimental results
on datasets from various domains demonstrate the effectiveness and efficiency
of the proposed method. Further experiments show that CoRec positively impacts
downstream tasks, improving the yield of state-of-the-art Open IE models.