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        <title>Semi Supervised Preposition-Sense Disambiguation using Multilingual Data</title>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Semi Supervised Preposition-Sense Disambiguation using Multilingual Data
%A Gonen, Hila
%A Goldberg, Yoav
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F gonen-goldberg-2016-semi
%X 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.
%U https://aclanthology.org/C16-1256
%P 2718-2729

Markdown (Informal)

[Semi Supervised Preposition-Sense Disambiguation using Multilingual Data](https://aclanthology.org/C16-1256) (Gonen & Goldberg, COLING 2016)

• Semi Supervised Preposition-Sense Disambiguation using Multilingual Data (Gonen & Goldberg, COLING 2016)

ACL

• Hila Gonen and Yoav Goldberg. 2016. Semi Supervised Preposition-Sense Disambiguation using Multilingual Data. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2718–2729, Osaka, Japan. The COLING 2016 Organizing Committee.