Current vector-space models of lexical semantics create a single "prototype" vector to represent the meaning of a word. However, due to lexical ambiguity, encoding word ...
We address the task of computing vector space representations for the meaning of word occurrences, which can vary widely according to context. This task is a crucial step towards ...
Standard IR systems can process queries such as “web NOT internet”, enabling users who are interested in arachnids to avoid documents about computing. The documents retrieved ...
Word space models, in the sense of vector space models built on distributional data taken from texts, are used to model semantic relations between words. We argue that the high dim...
Coecke, Sadrzadeh, and Clark [3] developed a compositional model of meaning for distributional semantics, in which each word in a sentence has a meaning vector and the distributio...
Edward Grefenstette, Mehrnoosh Sadrzadeh, Stephen ...