1,299 research outputs found
Sentence entailment in compositional distributional semantics
Distributional semantic models provide vector representations for words by
gathering co-occurrence frequencies from corpora of text. Compositional
distributional models extend these from words to phrases and sentences. In
categorical compositional distributional semantics, phrase and sentence
representations are functions of their grammatical structure and
representations of the words therein. In this setting, grammatical structures
are formalised by morphisms of a compact closed category and meanings of words
are formalised by objects of the same category. These can be instantiated in
the form of vectors or density matrices. This paper concerns the applications
of this model to phrase and sentence level entailment. We argue that
entropy-based distances of vectors and density matrices provide a good
candidate to measure word-level entailment, show the advantage of density
matrices over vectors for word level entailments, and prove that these
distances extend compositionally from words to phrases and sentences. We
exemplify our theoretical constructions on real data and a toy entailment
dataset and provide preliminary experimental evidence.Comment: 8 pages, 1 figure, 2 tables, short version presented in the
International Symposium on Artificial Intelligence and Mathematics (ISAIM),
201
Distributional Sentence Entailment Using Density Matrices
Categorical compositional distributional model of Coecke et al. (2010)
suggests a way to combine grammatical composition of the formal, type logical
models with the corpus based, empirical word representations of distributional
semantics. This paper contributes to the project by expanding the model to also
capture entailment relations. This is achieved by extending the representations
of words from points in meaning space to density operators, which are
probability distributions on the subspaces of the space. A symmetric measure of
similarity and an asymmetric measure of entailment is defined, where lexical
entailment is measured using von Neumann entropy, the quantum variant of
Kullback-Leibler divergence. Lexical entailment, combined with the composition
map on word representations, provides a method to obtain entailment relations
on the level of sentences. Truth theoretic and corpus-based examples are
provided.Comment: 11 page
Don't Blame Distributional Semantics if it can't do Entailment
Distributional semantics has had enormous empirical success in Computational
Linguistics and Cognitive Science in modeling various semantic phenomena, such
as semantic similarity, and distributional models are widely used in
state-of-the-art Natural Language Processing systems. However, the theoretical
status of distributional semantics within a broader theory of language and
cognition is still unclear: What does distributional semantics model? Can it
be, on its own, a fully adequate model of the meanings of linguistic
expressions? The standard answer is that distributional semantics is not fully
adequate in this regard, because it falls short on some of the central aspects
of formal semantic approaches: truth conditions, entailment, reference, and
certain aspects of compositionality. We argue that this standard answer rests
on a misconception: These aspects do not belong in a theory of expression
meaning, they are instead aspects of speaker meaning, i.e., communicative
intentions in a particular context. In a slogan: words do not refer, speakers
do. Clearing this up enables us to argue that distributional semantics on its
own is an adequate model of expression meaning. Our proposal sheds light on the
role of distributional semantics in a broader theory of language and cognition,
its relationship to formal semantics, and its place in computational models.Comment: To appear in Proceedings of the 13th International Conference on
Computational Semantics (IWCS 2019), Gothenburg, Swede
Semantics, Modelling, and the Problem of Representation of Meaning -- a Brief Survey of Recent Literature
Over the past 50 years many have debated what representation should be used
to capture the meaning of natural language utterances. Recently new needs of
such representations have been raised in research. Here I survey some of the
interesting representations suggested to answer for these new needs.Comment: 15 pages, no figure
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