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.

我们提出了一个名为 COordination RECognizer (CoRec) 的流水线模型，用于解决协调识别任务的挑战，该模型包含两个组件：协调识别器和从句边界检测器，并在来自各个领域的数据集上进行实验，证明了该方法的有效性和效率，进一步实验证明 CoRec 对于下游任务有积极影响，提高了最先进的 Open IE 模型的产出。