释义显义：揭示名词与复合词的隐性关系
Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations
摘要：
揭示名词复合词各成分之间隐含的语义关系对许多自然语言处理应用都很重要。它已被解决在文献中作为一个分类任务，以一组预定义的关系，或通过生产自由文本释义阐明的关系。现有的大多数释义方法缺乏泛化能力，难以解释不常见的或新的名词复合词。我们提出了一个神经模型，通过在连续空间中表示释义来更好地概括，概括了看不见的名词复合词和罕见的释义。我们的模型有助于提高名词复合词释义和分类任务的性能。
英文摘要：
Revealing the implicit semantic relation between the constituents of a noun-compound is important for many NLP applications. It has been addressed in the literature either as a classification task to a set of pre-defined relations or by producing free text paraphrases explicating the relations. Most existing paraphrasing methods lack the ability to generalize, and have a hard time interpreting infrequent or new noun-compounds. We propose a neural model that generalizes better by representing paraphrases in a continuous space, generalizing for both unseen noun-compounds and rare paraphrases. Our model helps improving performance on both the noun-compound paraphrasing and classification tasks.
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。
