论文标题
端到头中国词汇融合识别具有半知识
End to End Chinese Lexical Fusion Recognition with Sememe Knowledge
论文作者
论文摘要
在本文中，我们提出了中国词汇融合识别，这是一项新任务，可以被视为一种核心识别。首先，我们详细介绍了任务，显示了与核心识别的关系以及与现有任务的差异。其次，我们为任务提出了一个端到端的联合模型，该模型利用了最新的伯特表示形式作为编码器，并通过图形注意力网络从HOWNET中进一步增强了Sememe知识。我们为任务手动注释基准数据集，然后在其上进行实验。结果表明，我们的联合模型对任务有效且具有竞争力。提供详细的分析，以全面了解新任务和我们提出的模型。
In this paper, we present Chinese lexical fusion recognition, a new task which could be regarded as one kind of coreference recognition. First, we introduce the task in detail, showing the relationship with coreference recognition and differences from the existing tasks. Second, we propose an end-to-end joint model for the task, which exploits the state-of-the-art BERT representations as encoder, and is further enhanced with the sememe knowledge from HowNet by graph attention networks. We manually annotate a benchmark dataset for the task and then conduct experiments on it. Results demonstrate that our joint model is effective and competitive for the task. Detailed analysis is offered for comprehensively understanding the new task and our proposed model.
