Abstract
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
semantics.
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
semantics.
| Original language | Undefined/Unknown |
|---|---|
| Pages | 1263-1271 |
| Publication status | Published - 2010 |