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    <abstract>We present a successful collaboration of word embeddings and co-training to tackle in the most difficult test case of semantic role labeling: predicting out-of-domain and unseen semantic frames. Despite the fact that co-training is a successful traditional semi-supervised method, its application in SRL is very limited especially when a huge amount of labeled data is available. In this work, co-training is used together with word embeddings to improve the performance of a system trained on a large training dataset. We also introduce a semantic role labeling system with a simple learning architecture and effective inference that is easily adaptable to semi-supervised settings with new training data and/or new features. On the out-of-domain testing set of the standard benchmark CoNLL 2009 data our simple approach achieves high performance and improves state-of-the-art results.</abstract>
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%0 Conference Proceedings
%T Facing the most difficult case of Semantic Role Labeling: A collaboration of word embeddings and co-training
%A Do, Quynh Ngoc Thi
%A Bethard, Steven
%A Moens, Marie-Francine
%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 do-etal-2016-facing
%X We present a successful collaboration of word embeddings and co-training to tackle in the most difficult test case of semantic role labeling: predicting out-of-domain and unseen semantic frames. Despite the fact that co-training is a successful traditional semi-supervised method, its application in SRL is very limited especially when a huge amount of labeled data is available. In this work, co-training is used together with word embeddings to improve the performance of a system trained on a large training dataset. We also introduce a semantic role labeling system with a simple learning architecture and effective inference that is easily adaptable to semi-supervised settings with new training data and/or new features. On the out-of-domain testing set of the standard benchmark CoNLL 2009 data our simple approach achieves high performance and improves state-of-the-art results.
%U https://aclanthology.org/C16-1121/
%P 1275-1284

Markdown (Informal)

[Facing the most difficult case of Semantic Role Labeling: A collaboration of word embeddings and co-training](https://aclanthology.org/C16-1121/) (Do et al., COLING 2016)

• Facing the most difficult case of Semantic Role Labeling: A collaboration of word embeddings and co-training (Do et al., COLING 2016)

ACL

• Quynh Ngoc Thi Do, Steven Bethard, and Marie-Francine Moens. 2016. Facing the most difficult case of Semantic Role Labeling: A collaboration of word embeddings and co-training. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1275–1284, Osaka, Japan. The COLING 2016 Organizing Committee.