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Abstract / Description of output
Creating a language-independent meaning representation would benefit many cross-lingual NLP tasks. We introduce the first unsupervised approach to this problem, learning clusters of semantically equivalent English and French relations between referring expressions, based on their named-entity arguments in large monolingual corpora. The clusters can be used as language-independent semantic relations, by mapping clustered expressions in different languages onto the same relation. Our approach needs no parallel text for training, but outperforms a baseline that uses machine translation on a cross-lingual question answering task. We also show how to use the semantics to improve the accuracy of machine translation, by using it in a simple reranker.
Original language | English |
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Title of host publication | Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013, 18-21 October 2013, Grand Hyatt Seattle, Seattle, Washington, USA, A meeting of SIGDAT, a Special Interest Group of the ACL |
Publisher | Association for Computational Linguistics |
Pages | 681-692 |
Number of pages | 12 |
Publication status | Published - 2013 |
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Dive into the research topics of 'Unsupervised Induction of Cross-Lingual Semantic Relations'. Together they form a unique fingerprint.Projects
- 2 Finished
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Xperience - 'Robotes Bootstrapped through Learning from Experience'
Steedman, M., Geib, C. & Petrick, R.
1/01/10 → 31/12/15
Project: Research