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• W2559153497 abstract "Prepositions are very common and very ambiguous, and understanding their sense is critical for understanding the meaning of the sentence. Supervised corpora for the preposition-sense disambiguation task are small, suggesting a semi-supervised approach to the task. We show that signals from unannotated multilingual data can be used to improve supervised preposition-sense disambiguation. Our approach pre-trains an LSTM encoder for predicting the translation of a preposition, and then incorporates the pre-trained encoder as a component in a supervised classification system, and fine-tunes it for the task. The multilingual signals consistently improve results on two preposition-sense datasets." @default.
• W2559153497 created "2016-12-08" @default.
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• W2559153497 date "2016-11-27" @default.
• W2559153497 modified "2023-09-27" @default.
• W2559153497 title "Semi Supervised Preposition-Sense Disambiguation using Multilingual Data" @default.
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• W2559153497 hasPublicationYear "2016" @default.
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