Abstract
Computing the semantic similarity between terms (or short text expressions) that have the same meaning but which are not lexicographically similar is a key challenge in many computer related fields. The problem is that traditional approaches to semantic similarity measurement are not suitable for all situations, for example, many of them often fail to deal with terms not covered by synonym dictionaries or are not able to cope with acronyms, abbreviations, buzzwords, brand names, proper nouns, and so on. In this paper, we present and evaluate a collection of emerging techniques developed to avoid this problem. These techniques use some kinds of web intelligence to determine the degree of similarity between text expressions. These techniques implement a variety of paradigms including the study of co-occurrence, text snippet comparison, frequent pattern finding, or search log analysis. The goal is to substitute the traditional techniques where necessary.
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Martinez-Gil, J. An overview of textual semantic similarity measures based on web intelligence. Artif Intell Rev 42, 935–943 (2014). https://doi.org/10.1007/s10462-012-9349-8
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DOI: https://doi.org/10.1007/s10462-012-9349-8