Feeling is Understanding: From Affective to Semantic Spaces

Motivated by theories of language development we investigate the contribution of affect to lexical semantics in the context of distributional semantic models (DSMs). The relationship between semantic and affective spaces is computationally modeled for the task of semantic similarity computation between words. It is shown that affective spaces contain salient information for lexical semantic tasks. We further investigate specific semantic relationships whe re affective information plays a prominent role. The relations between semantic similarity and opposition are studied in the framework of a binary classification problem applied for the discrimination of synonyms and antonyms. For the case of antonyms, the use of affective features results in 33% relat ive improvement in classification accuracy compared to the use of semantic features.

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