Paper: Computing Semantic Compositionality in Distributional Semantics

ACL ID W11-0115
Title Computing Semantic Compositionality in Distributional Semantics
Venue IWCS
Session Main Conference
Year 2011
Authors

This article introduces and evaluates an approach to semantic compositionality in computational lin- guistics based on the combination of Distributional Semantics and supervised Machine Learning. In brief, distributional semantic spaces containing representations for complex constructions such as Adjective-Noun and Verb-Noun pairs, as well as for their constituent parts, are built. These repre- sentations are then used as feature vectors in a supervised learning model using multivariate multiple regression. In particular, the distributional semantic representations of the constituents are used to predict those of the complex structures. This approach outperforms the rivals in a series of experi- ments with Adjective-Noun pairs extracted from the BNC. In a second experimental setting based ...