Learning embeddings for transitive verb phrases
This repository includes the code to learn embeddings for transitive verb phrases using predicate-argument structures presented in our papers [1, 2]. You can reproduce the experimental results reported in our CVSC 2015 paper [2].
The training data used in our paper [2] is publicly available at our web site. You can put the training data in the train_data directory and run the code to train SVO embeddings.
[1] Kazuma Hashimoto, Pontus Stenetorp, Makoto Miwa, and Yoshimasa Tsuruoka. 2014. Jointly Learning Word Representations and Composition Functions Using Predicate-Argument Structures. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. pages 1544-1555.
[2] Kazuma Hashimoto and Yoshimasa Tsuruoka. 2015. Learning Embeddings for Transitive Verb Disambiguation by Implicit Tensor Factorization. In Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality. pages 1-11.
@InProceedings{hashimoto-EtAl:2014:EMNLP2014,
author = {Hashimoto, Kazuma and Stenetorp, Pontus and Miwa, Makoto and Tsuruoka, Yoshimasa},
title = {Jointly Learning Word Representations and Composition Functions Using Predicate-Argument Structures},
booktitle = {Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
month = {October},
year = {2014},
address = {Doha, Qatar},
publisher = {Association for Computational Linguistics},
pages = {1544--1555},
url = {http://www.aclweb.org/anthology/D14-1163}
}
@InProceedings{hashimoto-tsuruoka:2015:CVSC,
author = {Hashimoto, Kazuma and Tsuruoka, Yoshimasa},
title = {Learning Embeddings for Transitive Verb Disambiguation by Implicit Tensor Factorization},
booktitle = {Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality (CVSC)},
month = {July},
year = {2015},
address = {Beijing, China},
publisher = {Association for Computational Linguistics},
pages = {1--11},
url = {http://www.aclweb.org/anthology/W15-4001}
}
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