7,418 research outputs found
Compositional Distributional Semantics Models in Chunk-based Smoothed Tree Kernels
The field of compositional distributional
semantics has proposed very interesting
and reliable models for accounting the
distributional meaning of simple phrases.
These models however tend to disregard
the syntactic structures when they are applied
to larger sentences. In this paper we
propose the chunk-based smoothed tree
kernels (CSTKs) as a way to exploit the
syntactic structures as well as the reliability
of these compositional models for simple
phrases. We experiment with the recognizing
textual entailment datasets. Our
experiments show that our CSTKs perform
better than basic compositional distributional
semantic models (CDSMs) recursively
applied at the sentence level, and
also better than syntactic tree kernels
Resolving Lexical Ambiguity in Tensor Regression Models of Meaning
This paper provides a method for improving tensor-based compositional
distributional models of meaning by the addition of an explicit disambiguation
step prior to composition. In contrast with previous research where this
hypothesis has been successfully tested against relatively simple compositional
models, in our work we use a robust model trained with linear regression. The
results we get in two experiments show the superiority of the prior
disambiguation method and suggest that the effectiveness of this approach is
model-independent
Experimenting with Transitive Verbs in a DisCoCat
Formal and distributional semantic models offer complementary benefits in
modeling meaning. The categorical compositional distributional (DisCoCat) model
of meaning of Coecke et al. (arXiv:1003.4394v1 [cs.CL]) combines aspected of
both to provide a general framework in which meanings of words, obtained
distributionally, are composed using methods from the logical setting to form
sentence meaning. Concrete consequences of this general abstract setting and
applications to empirical data are under active study (Grefenstette et al.,
arxiv:1101.0309; Grefenstette and Sadrzadeh, arXiv:1106.4058v1 [cs.CL]). . In
this paper, we extend this study by examining transitive verbs, represented as
matrices in a DisCoCat. We discuss three ways of constructing such matrices,
and evaluate each method in a disambiguation task developed by Grefenstette and
Sadrzadeh (arXiv:1106.4058v1 [cs.CL]).Comment: 5 pages, to be presented at GEMS 2011, as part of EMNLP'11 workshop
From Logical to Distributional Models
The paper relates two variants of semantic models for natural language,
logical functional models and compositional distributional vector space models,
by transferring the logic and reasoning from the logical to the distributional
models.
The geometrical operations of quantum logic are reformulated as algebraic
operations on vectors. A map from functional models to vector space models
makes it possible to compare the meaning of sentences word by word.Comment: In Proceedings QPL 2013, arXiv:1412.791
Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning
Deep compositional models of meaning acting on distributional representations
of words in order to produce vectors of larger text constituents are evolving
to a popular area of NLP research. We detail a compositional distributional
framework based on a rich form of word embeddings that aims at facilitating the
interactions between words in the context of a sentence. Embeddings and
composition layers are jointly learned against a generic objective that
enhances the vectors with syntactic information from the surrounding context.
Furthermore, each word is associated with a number of senses, the most
plausible of which is selected dynamically during the composition process. We
evaluate the produced vectors qualitatively and quantitatively with positive
results. At the sentence level, the effectiveness of the framework is
demonstrated on the MSRPar task, for which we report results within the
state-of-the-art range.Comment: Accepted for presentation at EMNLP 201
A framework for Distributional Formal Semantics
Formal semantics and distributional semantics offer complementary strengths in capturing the meaning of natural language. As such, a considerable amount of research has sought to unify them, either by augmenting formal semantic systems with a distributional component, or by defining a formal system on top of distributed representations. Arriving at such a unified framework has, however, proven extremely challenging. One reason for this is that formal and distributional semantics operate on a fundamentally different `representational currency': formal semantics defines meaning in terms of models of the world, whereas distributional semantics defines meaning in terms of linguistic co-occurrence. Here, we pursue an alternative approach by deriving a vector space model that defines meaning in a distributed manner relative to formal models of the world. We will show that the resulting Distributional Formal Semantics offers probabilistic distributed representations that are also inherently compositional, and that naturally capture quantification and entailment. We moreover show that, when used as part of a neural network model, these representations allow for capturing incremental meaning construction and probabilistic inferencing. This framework thus lays the groundwork for an integrated distributional and formal approach to meaning
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