Context-theoretic Semantics for Natural Language: an Algebraic Framework
This work provides a theoretical foundation for vector-based semantics in computational linguistics, potentially benefiting researchers and practitioners in NLP, though it appears incremental as it builds on existing vector techniques.
The authors tackled the lack of a general semantic formalism for representing meaning as vectors in natural language processing by proposing an algebraic framework where words, phrases, and sentences are represented as vectors based on context, and they demonstrated applications such as incorporating logical semantics and analyzing textual entailment.
Techniques in which words are represented as vectors have proved useful in many applications in computational linguistics, however there is currently no general semantic formalism for representing meaning in terms of vectors. We present a framework for natural language semantics in which words, phrases and sentences are all represented as vectors, based on a theoretical analysis which assumes that meaning is determined by context. In the theoretical analysis, we define a corpus model as a mathematical abstraction of a text corpus. The meaning of a string of words is assumed to be a vector representing the contexts it occurs in in the corpus model. Based on this assumption, we can show that the vector representations of words can be considered as elements of an algebra over a field. We note that in applications of vector spaces to representing meanings of words there is an underlying lattice structure; we interpret the partial ordering of the lattice as describing entailment between meanings. We also define the context-theoretic probability of a string, and, based on this and the lattice structure, a degree of entailment between strings. Together these properties form guidelines as to how to construct semantic representations within the framework. A context theory is an implementation of the framework; in an implementation strings are represented as vectors with the properties deduced from the theoretical analysis. We show how to incorporate logical semantics into context theories; this enables us to represent statistical information about uncertainty by taking weighted sums of individual representations. We also use the framework to analyse approaches to the task of recognising textual entailment, to ontological representations of meaning and to representing syntactic structure. For the latter, we give new algebraic descriptions of link grammar.