@inproceedings{han-etal-2017-dependency,
title = "Dependency Grammar Induction with Neural Lexicalization and Big Training Data",
author = "Han, Wenjuan and
Jiang, Yong and
Tu, Kewei",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1176",
doi = "10.18653/v1/D17-1176",
pages = "1683--1688",
abstract = "We study the impact of big models (in terms of the degree of lexicalization) and big data (in terms of the training corpus size) on dependency grammar induction. We experimented with L-DMV, a lexicalized version of Dependency Model with Valence (Klein and Manning, 2004) and L-NDMV, our lexicalized extension of the Neural Dependency Model with Valence (Jiang et al., 2016). We find that L-DMV only benefits from very small degrees of lexicalization and moderate sizes of training corpora. L-NDMV can benefit from big training data and lexicalization of greater degrees, especially when enhanced with good model initialization, and it achieves a result that is competitive with the current state-of-the-art.",
}
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%0 Conference Proceedings
%T Dependency Grammar Induction with Neural Lexicalization and Big Training Data
%A Han, Wenjuan
%A Jiang, Yong
%A Tu, Kewei
%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 han-etal-2017-dependency
%X We study the impact of big models (in terms of the degree of lexicalization) and big data (in terms of the training corpus size) on dependency grammar induction. We experimented with L-DMV, a lexicalized version of Dependency Model with Valence (Klein and Manning, 2004) and L-NDMV, our lexicalized extension of the Neural Dependency Model with Valence (Jiang et al., 2016). We find that L-DMV only benefits from very small degrees of lexicalization and moderate sizes of training corpora. L-NDMV can benefit from big training data and lexicalization of greater degrees, especially when enhanced with good model initialization, and it achieves a result that is competitive with the current state-of-the-art.
%R 10.18653/v1/D17-1176
%U https://aclanthology.org/D17-1176
%U https://doi.org/10.18653/v1/D17-1176
%P 1683-1688
Markdown (Informal)
[Dependency Grammar Induction with Neural Lexicalization and Big Training Data](https://aclanthology.org/D17-1176) (Han et al., EMNLP 2017)
ACL