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- W2760314189 abstract "Idioms are peculiar linguistic constructions that impose great challenges for representing the semantics of language, especially in current prevailing end-to-end neural models, which assume that the semantics of a phrase or sentence can be literally composed from its constitutive words. In this paper, we propose an idiom-aware distributed semantic model to build representation of sentences on the basis of understanding their contained idioms. Our models are grounded in the literal-first psycholinguistic hypothesis, which can adaptively learn semantic compositionality of a phrase literally or idiomatically. To better evaluate our models, we also construct an idiom-enriched sentiment classification dataset with considerable scale and abundant peculiarities of idioms. The qualitative and quantitative experimental analyses demonstrate the efficacy of our models." @default.
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- W2760314189 date "2017-01-01" @default.
- W2760314189 modified "2023-09-24" @default.
- W2760314189 title "Idiom-Aware Compositional Distributed Semantics" @default.
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- W2760314189 doi "https://doi.org/10.18653/v1/d17-1124" @default.
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