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Hits 1 – 2 of 2
1
Morph-fitting: Fine-tuning word vector spaces with simple language-specific rules ...
Vulic, Ivan
;
Mrkšic, N
;
Reichart, R
. - : Apollo - University of Cambridge Repository, 2017
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2
Morph-fitting: Fine-tuning word vector spaces with simple language-specific rules
Vulic, Ivan
;
Mrkšic, N
;
Reichart, R
;
Séaghdha, D
;
Young, Steve
;
Korhonen, Anna-Leena
. - : Association for Computational Linguistics, 2017. : ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), 2017
Abstract:
Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms; and 2) insensitivity to distinct lexical relations that have similar distributional signatures. These effects are detrimental for language understanding systems, which may infer that inexpensive is a rephrasing for expensive or may not associate acquire with acquires. In this work, we propose a novel morph-fitting procedure which moves past the use of curated semantic lexicons for improving distributional vector spaces. Instead, our method injects morphological constraints generated using simple language-specific rules, pulling inflectional forms of the same word close together and pushing derivational antonyms far apart. In intrinsic evaluation over four languages, we show that our approach: 1) improves low-frequency word estimates; and 2) boosts the semantic quality of the entire word vector collection. Finally, we show that morph-fitted vectors yield large gains in the downstream task of dialogue state tracking, highlighting the importance of morphology for tackling long-tail phenomena in language understanding tasks.
Keyword:
Dialogue state tracking
;
Morphologically complex languages
;
Semantic specialisation
;
Vector space models
;
Word embeddings
URL:
https://doi.org/10.17863/CAM.10176
https://www.repository.cam.ac.uk/handle/1810/264637
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