Beekhuizen, B., Milic, S., Armstrong, B. C., & Stevenson, S.
(2018). What Company Do Semantically Ambiguous Words Keep? Insights
from Distributional Word Vectors. Proceedings of the 40th Annual
Conference of the Cognitive Science Society. Mahwah, NH: Lawrence
Erlbaum Associates.
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Abstract
The diversity of a word’s contexts affects its acquisition and processing. Can differences between word types such as monosemes (unambiguous words), polysemes (multiple related senses), and homonyms (multiple unrelated meanings) be related to distributional properties of these words? We tested for traces of number and relatedness of meaning in vector representations by comparing the distance between words of each type and vector representations of various “contexts”: their dictionary definitions (an extreme disambiguating context), their use in film subtitles (a natural context), and their semantic neighbours in vector space (a vector-space-internal context). Whereas dictionary definitions reveal a three-way split between our word types, the other two contexts produced a two-way split between ambiguous and unambiguous words. These inconsistencies align with some discrepancies in behavioural studies and present a paradox regarding how models learn meaning relatedness despite natural contexts seemingly lacking such relatedness. We argue that viewing ambiguity as a continuum could resolve many of these issues.
Keywords: lexical/semantic ambiguity; homonymy; polysemy; vector space models; contextual diversity.
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