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A Latent Variable Model . . . (2009)
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@MISC{Gesmundo09alatent,
author = {Andrea Gesmundo and James Henderson and Paola Merlo and Ivan Titov},
title = {A Latent Variable Model . . . },
year = {2009}
}
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Abstract
Motivated by the large number of languages (seven) and the short development time (two months) of the 2009 CoNLL shared task, we exploited latent variables to avoid the costly process of hand-crafted feature engineering, allowing the latent variables to induce features from the data. We took a pre-existing generative latent variable model of joint syntacticsemantic dependency parsing, developed for English, and applied it to six new languages with minimal adjustments. The parser’s robustness across languages indicates that this parser has a very general feature set. The parser’s high performance indicates that its latent variables succeeded in inducing effective features. This system was ranked third overall with a macro averaged F1 score of 82.14%, only 0.5 % worse than the best system.
Keyphrases
latent variable model latent variable hand-crafted feature engineering new language costly process parser high performance short development time large number f1 score joint syntacticsemantic dependency parsing effective feature minimal adjustment pre-existing generative latent variable model parser robustness general feature
