35 research outputs found
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
Multilingual Models for Compositional Distributed Semantics
We present a novel technique for learning semantic representations, which
extends the distributional hypothesis to multilingual data and joint-space
embeddings. Our models leverage parallel data and learn to strongly align the
embeddings of semantically equivalent sentences, while maintaining sufficient
distance between those of dissimilar sentences. The models do not rely on word
alignments or any syntactic information and are successfully applied to a
number of diverse languages. We extend our approach to learn semantic
representations at the document level, too. We evaluate these models on two
cross-lingual document classification tasks, outperforming the prior state of
the art. Through qualitative analysis and the study of pivoting effects we
demonstrate that our representations are semantically plausible and can capture
semantic relationships across languages without parallel data.Comment: Proceedings of ACL 2014 (Long papers
Experimental Support for a Categorical Compositional Distributional Model of Meaning
Modelling compositional meaning for sentences using empirical distributional
methods has been a challenge for computational linguists. We implement the
abstract categorical model of Coecke et al. (arXiv:1003.4394v1 [cs.CL]) using
data from the BNC and evaluate it. The implementation is based on unsupervised
learning of matrices for relational words and applying them to the vectors of
their arguments. The evaluation is based on the word disambiguation task
developed by Mitchell and Lapata (2008) for intransitive sentences, and on a
similar new experiment designed for transitive sentences. Our model matches the
results of its competitors in the first experiment, and betters them in the
second. The general improvement in results with increase in syntactic
complexity showcases the compositional power of our model.Comment: 11 pages, to be presented at EMNLP 2011, to be published in
Proceedings of the 2011 Conference on Empirical Methods in Natural Language
Processin
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
A lot of the recent success in natural language processing (NLP) has been
driven by distributed vector representations of words trained on large amounts
of text in an unsupervised manner. These representations are typically used as
general purpose features for words across a range of NLP problems. However,
extending this success to learning representations of sequences of words, such
as sentences, remains an open problem. Recent work has explored unsupervised as
well as supervised learning techniques with different training objectives to
learn general purpose fixed-length sentence representations. In this work, we
present a simple, effective multi-task learning framework for sentence
representations that combines the inductive biases of diverse training
objectives in a single model. We train this model on several data sources with
multiple training objectives on over 100 million sentences. Extensive
experiments demonstrate that sharing a single recurrent sentence encoder across
weakly related tasks leads to consistent improvements over previous methods. We
present substantial improvements in the context of transfer learning and
low-resource settings using our learned general-purpose representations.Comment: Accepted at ICLR 201
Mathematical Foundations for a Compositional Distributional Model of Meaning
We propose a mathematical framework for a unification of the distributional
theory of meaning in terms of vector space models, and a compositional theory
for grammatical types, for which we rely on the algebra of Pregroups,
introduced by Lambek. This mathematical framework enables us to compute the
meaning of a well-typed sentence from the meanings of its constituents.
Concretely, the type reductions of Pregroups are `lifted' to morphisms in a
category, a procedure that transforms meanings of constituents into a meaning
of the (well-typed) whole. Importantly, meanings of whole sentences live in a
single space, independent of the grammatical structure of the sentence. Hence
the inner-product can be used to compare meanings of arbitrary sentences, as it
is for comparing the meanings of words in the distributional model. The
mathematical structure we employ admits a purely diagrammatic calculus which
exposes how the information flows between the words in a sentence in order to
make up the meaning of the whole sentence. A variation of our `categorical
model' which involves constraining the scalars of the vector spaces to the
semiring of Booleans results in a Montague-style Boolean-valued semantics.Comment: to appea