Context-theoretic Semantics for Natural Language: an Overview

By Daoud Clarke. 2009

84 citations recorded by the source corpus.

We present the context-theoretic framework, which provides a set of rules for the nature of composition of meaning based on the philosophy of meaning as context. Principally, in the framework the composition of the meaning of words can be represented as multiplication of their representative vectors, where multiplication is distributive with respect to the vector space. We discuss the applicability of the framework to a range of techniques in natural language processing, including subsequence matching, the lexical entailment model of Dagan et al. (2005), vector-based representations of taxonomies, statistical parsing and the representation of uncertainty in logical semantics.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Natural Language Processing

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Citations in the corpus

From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions (2014, background): “More recently, asymmetric similarities have been proposed as more suitable for semantic inference tasks such as entailment (Weeds and Weir, 2003; Szpektor and Dagan, 2008; Clarke, 2009; Kotlerman et al., 2010).”
Do Supervised Distributional Methods Really Learn Lexical Inference Relations? (2015, background): “Many unsupervised distributional methods of recognizing lexical inference replace cosine similarity with an asymmetric similarity function (Weeds and Weir, 2003; Clarke, 2009; Kotlerman et al., 2010; Santus et al., 2014).”
Learning Semantic Hierarchies via Word Embeddings (2014, background): “Besides Kotlerman et al. (2010) and Lenci and Benotto (2012), other researchers also propose directional distributional similarity methods (Weeds et al., 2004; Geffet and Dagan, 2005; Bhagat et al., 2007; Szpektor et al., 2007; Clarke, 2009).”
A Survey of Paraphrasing and Textual Entailment Methods (2009, background): “…input expressions to a single vector that attempts to capture its meaning; in the simplest case, the vector of each expression could be the sum or product of the vectors of its words, but more elaborate approaches have also been proposed (Mitchell & Lapata, 2008; Erk & Padó, 2009; Clarke, 2009).”
Directional distributional similarity for lexical inference (2010, background): “In a recent work of Clarke (2009) the degree of entailment measure, denoted here as ClarkeDE , was proposed: It quantiﬁes weighted coverage of the features of the candidate narrower term u by the features of the broader term v and thus resembles the WeedsPrec measure.”