tESA: a distributional measure for calculating semantic relatedness

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tESA: a distributional measure for calculating semantic relatedness

BACKGROUND Semantic relatedness is a measure that quantifies the strength of a semantic link between two concepts. Often, it can be efficiently approximated with methods that operate on words, which represent these concepts. Approximating semantic relatedness between texts and concepts represented by these texts is an important part of many text and knowledge processing tasks of crucial importa...

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Supervised Distributional Semantic Relatedness

Distributional measures of semantic relatedness determine word similarity based on how frequently a pair of words appear in the same contexts. A typical method is to construct a word-context matrix, then re-weight it using some measure of association, and finally take the vector distance as a measure of similarity. This has largely been an unsupervised process, but in recent years more work has...

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Distributional Measures as Proxies for Semantic Relatedness

The automatic ranking of word pairs as per their semantic relatedness and ability to mimic human notions of semantic relatedness has widespread applications. Measures that rely on raw data (distributional measures) and those that use knowledge-rich ontologies both exist. Although extensive studies have been performed to compare ontological measures with human judgment, the distributional measur...

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Potential and limits of distributional approaches for semantic relatedness

Distributional models assume that the contexts of a linguistic unit (such as a word, a multi-word expression, a phrase, a sentence, etc.) provide information about the meaning of the linguistic unit (Firth, 1957; Harris, 1968). They have been widely applied in data-intensive lexical semantics (among other areas), and proven successful in diverse research issues, such as the representation and d...

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ژورنال

عنوان ژورنال: Journal of Biomedical Semantics

سال: 2016

ISSN: 2041-1480

DOI: 10.1186/s13326-016-0109-6