Estimating Linear Models for Compositional Distributional Semantics

Fabio Massimo Zanzotto, Ioannis Korkontzelos, Francesca Fallucchi, Suresh Manandhar

Research output: Contribution to conferencePaperpeer-review


In distributional semantics studies, there
is a growing attention in compositionally
determining the distributional meaning of
word sequences. Yet, compositional distributional
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 compositional
distributional models: the additive
models. Our approach leverages on
two main ideas. Firstly, a novel idea for
extracting compositional distributional semantics
examples. Secondly, an estimation
method based on regression models
for multiple dependent variables. Experiments
demonstrate that our approach outperforms
existing methods for determining
a good model for compositional distributional
Original languageUndefined/Unknown
Publication statusPublished - 2010

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