Paper: Estimating Linear Models for Compositional Distributional Semantics

ACL ID C10-1142
Title Estimating Linear Models for Compositional Distributional Semantics
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010
Authors

In distributional semantics studies, there is a growing attention in compositionally determining the distributional meaning of word sequences. Yet, compositional dis- tributional models depend on a large set of parameters that have not been explored. In this paper we propose a novel approach to estimate parameters for a class of com- positional distributional models: the addi- tive models. Our approach leverages on two main ideas. Firstly, a novel idea for extracting compositional distributional se- mantics examples. Secondly, an estima- tion method based on regression models for multiple dependent variables. Experi- ments demonstrate that our approach out- performs existing methods for determin- ing a good model for compositional dis- tributional semantics.