名词性复合词链抽取：语义丰富词汇链的新课题
Nominal Compound Chain Extraction: A New Task for Semantic-enriched Lexical Chain
摘要：
词汇链由文档中的衔接词组成，它暗示了文本的底层结构，从而有助于下游的NLP任务。然而，现有的工作主要集中在检测具有浅层句法关联的简单表层词汇，而忽略了语义感知的词汇复合词以及潜在的语义框架，主题），这对于现实世界的NLP应用程序来说可能更为重要。在本文中，我们介绍了一种新的任务，名词复合词链提取（NCCE），提取和聚类所有名词复合词，共享相同的语义主题。此外，我们将任务建模为两阶段预测（即，复合提取和链检测），其经由所提出的联合框架来处理。该模型采用BERT编码器来产生上下文化的文档表示。此外，知网作为外部资源，提供丰富的义素信息。实验基于我们的人工标注语料库，结果证明了NCCE任务的必要性以及我们的联合方法的有效性。
英文摘要：
Lexical chain consists of cohesion words in a document, which implies the underlying structure of a text, and thus facilitates downstream NLP tasks. Nevertheless, existing work focuses on detecting the simple surface lexicons with shallow syntax associations, ignoring the semantic-aware lexical compounds as well as the latent semantic frames, (e.g., topic), which can be much more crucial for real-world NLP applications. In this paper, we introduce a novel task, Nominal Compound Chain Extraction (NCCE), extracting and clustering all the nominal compounds that share identical semantic topics. In addition, we model the task as a two-stage prediction (i.e., compound extraction and chain detection), which is handled via a proposed joint framework. The model employs the BERT encoder to yield contextualized document representation. Also, HowNet is exploited as external resources for offering rich sememe information. The experiments are based on our manually annotated corpus, and the results prove the necessity of the NCCE task as well as the effectiveness of our joint approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
