In Latent Semantic Analysis (LSA) the meaning of a word is represented as a vector in a high-dimensional semantic space. Different meanings of a word or different senses of a word are not distinguished. Instead, word senses are appropriately modified as the word is used in different contexts. In N-VP sentences, the precise meaning of the verb phrase depends on the noun it is combined with. An a...

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 meaning with a single vector is problematic. This paper presents a method that uses clustering to produce multiple “sense-specific” vectors for each word. This approach provides a context-dependent vector representation of word ...

Taking the stance that two well-known word learning biases (shape and material bias) are formed through learning (learned bias account, LBA), we illustrated a concrete computational mechanism with " ad-hoc meaning substitution (AMS) " hypothesis, and verified it by two computer simulations. AMS represents that when given a novel word and a corresponding instance, children create novel word mean...

Choosing the right word is difficult. One reason is that the context affects the meaning expressed by a word in complex ways. In particular, when a word is used in a context that is not normal for the word, it may evoke a special meaning. This paper presents a lexical choice process that chooses the word from a set of near-synonyms that best produces the desired effects in the given context. It...