Semantic Measures

Semantic Measures

Yoan Chabot (University of Burgundy, France) and Christophe Nicolle (University of Burgundy, France)
DOI: 10.4018/978-1-4666-5888-2.ch460
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Background

Similarity and Semantic Relatedness

This section discusses the three different types of semantic measures. Depending on the applications and the developers’ needs, proposals may be semantic relatedness measures, semantic similarity measures or semantic distance. The semantic measure, which allows to quantify the distance between the meanings of two concepts, is a generic term covering several concepts (Budanitsky & Hirst, 2006) (Gracia & Mena, 2008):

  • Semantic relatedness covers all possible semantic relationships. This is a broader measure than the measure of semantic similarity. Indeed, the terms which do not share a common meaning can be considered semantically close, as they can be linked by a meronym or antonym relationship. They can also be linked by a functional relationship or frequent association relationship (e.g. “car” and “road,” “lion” and “Africa”).

  • Semantic similarity is a special case of semantic relatedness. This distance uses only synonymy, hyponymy and hyperonymy relationships to determine whether two words share common characteristics.

  • Semantic distance is often viewed as the inverse of semantic relatedness or semantic similarity. If the proximity increases, the semantic distance decreases. In most cases, the two “visions” of the term “distance” are compatible. However, there are exceptions. For example, antonym concepts are semantically dissimilar but still very close, due to the antonymous relationship. Generally, it is accepted that semantic distance is the inverse of semantic relatedness.

Key Terms in this Chapter

Information Retrieval: Field of information technology whose aim is to provide techniques to process queries for extracting information from corpus.

Knowledge Engineering: Field of information technology whose aim is to provide techniques to store and manipulate knowledge.

Ontology: Model of knowledge representation used especially in the areas of Semantic Web and artificial intelligence. Ontologies are used to represent domain knowledge using concepts, relations and axioms.

Semantic: As opposed to syntax, semantic defines the mental representation of concepts corresponding to the symbols used in texts or images.

Semantic Similarity: A semantic measure which is a special case of semantic relatedness. This distance uses only synonymy, hyponymy and hyperonymy relationships, and determines whether two words share common characteristics.

Semantic Relatedness: This is a broader measure than the measure of semantic similarity. Indeed, the terms which do not share a common meaning can be considered semantically close, as they can be linked by a meronym or antonym relationship.

Natural Language Processing: Field of information technology to provide methods and algorithms for processing human language. Automatic translation tools, spelling checkers, or even speech recognition software are among the most popular applications.

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