Publisher = "Association for Computational Linguistics",Ībstract = "Word composition is a promising technique for representation learning of large linguistic units (e.g., phrases, sentences and documents). Cite (Informal): Model-Free Context-Aware Word Composition (An et al., COLING 2018) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: = "Model-Free Context-Aware Word Composition",īooktitle = "Proceedings of the 27th International Conference on Computational Linguistics", Association for Computational Linguistics. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2834–2845, Santa Fe, New Mexico, USA. Model-Free Context-Aware Word Composition.
Anthology ID: C18-1240 Volume: Proceedings of the 27th International Conference on Computational Linguistics Month: August Year: 2018 Address: Santa Fe, New Mexico, USA Venue: COLING SIG: Publisher: Association for Computational Linguistics Note: Pages: 2834–2845 Language: URL: DOI: Bibkey: an-etal-2018-model Cite (ACL): Bo An, Xianpei Han, and Le Sun. Extensive evaluation shows consistent improvements over various strong word representation/composition models at different granularities (including word, phrase and sentence), demonstrating the effectiveness of our proposed method. The proposed model attempts to resolve the word sense disambiguation and word composition in a unified framework. To address this issue, we propose a model-free context-aware word composition model, which employs the latent semantic information as global context for learning representations. However, most of the current composition models do not take the ambiguity of words and the context outside of a linguistic unit into consideration for learning representations, and consequently suffer from the inaccurate representation of semantics.
Abstract Word composition is a promising technique for representation learning of large linguistic units (e.g., phrases, sentences and documents).
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