Distributional hypothesis as isomorphism between word-word co-occurrence and analogical parallelograms.

ABSTRACT: Most of the modern natural language processing (NLP) techniques are based on the vector space models of language, in which each word is represented by a vector in a high dimensional space. One of the earliest successes was demonstrated by the four-term analogical reasoning task: what is to C as B is to A? The trained word vectors form "parallelograms" representing the quadruple of words in analogy. This discovery in NLP offers us insight into our understanding of human semantic representation of words via analogical reasoning. Despite successful applications of the large-scale language models, it has not been fully understood why such parallelograms emerge by learning through natural language data. As the vector space model is not optimized to form parallelograms, the key structure related

SUBMITTER: Torii T 

PROVIDER: S-EPMC11493305 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature