When natural language phrases are combined, their meaning is often more than the sum of their parts. In the context of NLP tasks such as sentiment analysis, where the meaning of a phrase is its sentiment, that still applies. Many NLP studies on sentiment analysis, however, focus on the fact that sentiment computations are largely compositional. We, instead, set out to obtain non-compositionality ratings for phrases with respect to their sentiment. Our contributions are as follows: a) a methodology for obtaining those non-compositionality ratings, b) a resource of ratings for 259 phrases -- NonCompSST -- along with an analysis of that resource, and c) an evaluation of computational models for sentiment analysis using this new resource.

翻译：当自然语言短语组合时，其含义往往超越各组成部分的简单叠加。在情感分析等自然语言处理任务中，短语的意义即其情感，这一现象同样适用。然而，许多关于情感分析的NLP研究侧重于情感计算主要具有组合性这一事实。我们则致力于获取短语在情感维度上的非组合性评分。本文贡献如下：a) 提出获取非组合性评分的方法论；b) 构建包含259个短语的评分资源库——NonCompSST——并对此资源进行分析；c) 利用这一新资源评估情感分析计算模型。