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    <abstract>Annotating large numbers of sentences with senses is the heaviest requirement of current Word Sense Disambiguation. We present Train-O-Matic, a language-independent method for generating millions of sense-annotated training instances for virtually all meanings of words in a language’s vocabulary. The approach is fully automatic: no human intervention is required and the only type of human knowledge used is a WordNet-like resource. Train-O-Matic achieves consistently state-of-the-art performance across gold standard datasets and languages, while at the same time removing the burden of manual annotation. All the training data is available for research purposes at http://trainomatic.org.</abstract>
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%0 Conference Proceedings
%T Train-O-Matic: Large-Scale Supervised Word Sense Disambiguation in Multiple Languages without Manual Training Data
%A Pasini, Tommaso
%A Navigli, Roberto
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F pasini-navigli-2017-train
%X Annotating large numbers of sentences with senses is the heaviest requirement of current Word Sense Disambiguation. We present Train-O-Matic, a language-independent method for generating millions of sense-annotated training instances for virtually all meanings of words in a language’s vocabulary. The approach is fully automatic: no human intervention is required and the only type of human knowledge used is a WordNet-like resource. Train-O-Matic achieves consistently state-of-the-art performance across gold standard datasets and languages, while at the same time removing the burden of manual annotation. All the training data is available for research purposes at http://trainomatic.org.
%R 10.18653/v1/D17-1008
%U https://aclanthology.org/D17-1008
%U https://doi.org/10.18653/v1/D17-1008
%P 78-88

Markdown (Informal)

[Train-O-Matic: Large-Scale Supervised Word Sense Disambiguation in Multiple Languages without Manual Training Data](https://aclanthology.org/D17-1008) (Pasini & Navigli, EMNLP 2017)

• Train-O-Matic: Large-Scale Supervised Word Sense Disambiguation in Multiple Languages without Manual Training Data (Pasini & Navigli, EMNLP 2017)

ACL

• Tommaso Pasini and Roberto Navigli. 2017. Train-O-Matic: Large-Scale Supervised Word Sense Disambiguation in Multiple Languages without Manual Training Data. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 78–88, Copenhagen, Denmark. Association for Computational Linguistics.