Multi-Step Regression Learning for Compositional Distributional Semantics
Computation and Language
2013-01-31 v2 Machine Learning
Abstract
We present a model for compositional distributional semantics related to the framework of Coecke et al. (2010), and emulating formal semantics by representing functions as tensors and arguments as vectors. We introduce a new learning method for tensors, generalising the approach of Baroni and Zamparelli (2010). We evaluate it on two benchmark data sets, and find it to outperform existing leading methods. We argue in our analysis that the nature of this learning method also renders it suitable for solving more subtle problems compositional distributional models might face.
Keywords
Cite
@article{arxiv.1301.6939,
title = {Multi-Step Regression Learning for Compositional Distributional Semantics},
author = {Edward Grefenstette and Georgiana Dinu and Yao-Zhong Zhang and Mehrnoosh Sadrzadeh and Marco Baroni},
journal= {arXiv preprint arXiv:1301.6939},
year = {2013}
}
Comments
10 pages + 1 page references, to be presented at the 10th International Conference on Computational Semantics (IWCS 2013)