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SyMSS: A syntax-based measure for short-text semantic similarity

机译：SyMSS：一种基于语法的短文本语义相似性度量

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摘要

Sentence and short-text semantic similarity measures are becoming an important part of many natural language processing tasks, such as text summarization and conversational agents. This paper presents SyMSS, a new method for computing short-text and sentence semantic similarity. The method is based on the notion that the meaning of a sentence is made up of not only the meanings of its individual words, but also the structural way the words are combined. Thus, SyMSS captures and combines syntactic and semantic information to compute the semantic similarity of two sentences. Semantic information is obtained from a lexical database. Syntactic information is obtained through a deep parsing process that finds the phrases in each sentence. With this information, the proposed method measures the semantic similarity between concepts that play the same syntactic role. Psychological plausibility is added to the method by using previous findings about how humans weight different syntactic roles when computing semantic similarity. The results show that SyMSS outperforms state-of-the-art methods in terms of rank correlation with human intuition, thus proving the importance of syntactic information in sentence semantic similarity computation.
机译：句子和短文本语义相似性度量正在成为许多自然语言处理任务的重要组成部分，例如文本摘要和会话代理。本文介绍了SyMSS，一种计算短文本和句子语义相似度的新方法。该方法基于这样的概念，即句子的含义不仅由其各个单词的含义组成，而且还由单词组合的结构方式组成。因此，SyMSS捕获并组合了句法和语义信息以计算两个句子的语义相似度。语义信息是从词汇数据库中获得的。语法信息是通过深度解析过程获得的，该过程在每个句子中找到短语。利用这些信息，所提出的方法可以测量起相同语法作用的概念之间的语义相似性。通过使用关于人类在计算语义相似度时如何权衡不同的句法角色的先前发现，将心理学上的合理性添加到该方法中。结果表明，SyMSS在与人类直觉的等级相关性方面优于最新方法，从而证明了句法信息在句子语义相似度计算中的重要性。

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