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.- Anthology ID:
- D17-1124
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1204–1213
- Language:
- URL:
- https://aclanthology.org/D17-1124
- DOI:
- 10.18653/v1/D17-1124
- Cite (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.
- Cite (Informal):
- Idiom-Aware Compositional Distributed Semantics (Liu et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/D17-1124.pdf
- Data
- SST, SST-2