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Valuing Semantic Relatedness

机译：重视语义相关性

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摘要

Semantic Relatedness is widely used in various domains such as DNA sequence analysis, knowledge representation, natural language processing, data mining, information retrieval, information flow etc... Computing semantic similarity between two entities is a non-trivial task. There are many ways to define semantic similarity. Some measures have been proposed combining both statistical information and lexical similarity. It is difficult for a measure that performs well in a given domain to be applied with accuracy in another domain. A similarity measure may perform better with one language than another. Word is supposed to be not only similar to itself but also to some of its synonyms in a given context, and some words with common roots. Our approach is designed to perform query matching and compute semantic relatedness using word occurrences. It performs better than classical measures like TF-IDF, Cosine etc... Although it is not a metric, the proposed similarity measure can be used for a wide range of content analysis tasks based on semantic distance and its efficacy has been demonstrated. The measure is not corpus dependent so it can establish directly the se-mantic relatedness of two entities.
机译：语义相关性广泛用于DNA序列分析，知识表示，自然语言处理，数据挖掘，信息检索，信息流等的各个域中......计算两个实体之间的语义相似性是非琐碎的任务。有很多方法可以定义语义相似之处。已经提出了一些措施，结合了统计信息和词汇相似性。在给定域中在特定域中进行良好的度量难以在另一个域中应用。相似性度量可以用一种语言来表现比另一个语言更好。 Word应该不仅与本身相似，而且是在给定的上下文中的一些同义词，以及一些具有常见根源的单词。我们的方法旨在使用Word Icallence执行查询匹配和计算语义相关性。它比TF-IDF，余弦等更好地表现得更好，但是它不是指标，所提出的相似度测量可以用于基于语义距离的广泛的内容分析任务，并且已经证明了其功效。该措施不是依赖的毒品，因此它可以直接建立两个实体的SE-Mantic相关性。

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