@inproceedings{liu-etal-2017-idiom,
title = "Idiom-Aware Compositional Distributed Semantics",
author = "Liu, Pengfei and
Qian, Kaiyu and
Qiu, Xipeng and
Huang, Xuanjing",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1124",
doi = "10.18653/v1/D17-1124",
pages = "1204--1213",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Idiom-Aware Compositional Distributed Semantics
%A Liu, Pengfei
%A Qian, Kaiyu
%A Qiu, Xipeng
%A Huang, Xuanjing
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F liu-etal-2017-idiom
%X 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.
%R 10.18653/v1/D17-1124
%U https://aclanthology.org/D17-1124
%U https://doi.org/10.18653/v1/D17-1124
%P 1204-1213
Markdown (Informal)
[Idiom-Aware Compositional Distributed Semantics](https://aclanthology.org/D17-1124) (Liu et al., EMNLP 2017)
ACL
- Pengfei Liu, Kaiyu Qian, Xipeng Qiu, and Xuanjing Huang. 2017. Idiom-Aware Compositional Distributed Semantics. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1204–1213, Copenhagen, Denmark. Association for Computational Linguistics.