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Modelling Lexical Ambiguity in Computers, With and Without Word Senses| old_uid | 13740 |
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| title | Modelling Lexical Ambiguity in Computers, With and Without Word Senses |
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| start_date | 2014/04/01 |
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| schedule | 14h |
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| online | no |
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| location_info | salle de conférences |
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| summary | In Computational Linguistics, there is a growing body of work that
models word meaning from corpus data without recourse to a hand-crafted
inventory. This has several advantages, including focusing computers on
meanings which are attested in the data, but there is still the issue of
whether word usages are best clustered into word senses or whether some
other representation is better. In this talk I will describe a few
projects using topic models and vector space models acquired from corpus
data for representing word meaning. I will demonstrate how these models
can be used both with and without an explicit representation of word
sense. In applications such as novel sense detection for lexicography,
representation of word sense is necessary, while for similarity
judgements a more dynamic representation outperforms the
alternatives. Linguists have advocated that a clear cut division into
senses is not appropriate for all words, and that there is a spectrum of
behaviour from clear cut cases of ambiguity to vague words with
inter-related meanings. I will end with a brief introduction to current
work which aims to identify empirically where on this spectrum a given lemma is. |
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| responsibles | Paroubek, Turner |
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