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        <title>Improved CCG Parsing with Semi-supervised Supertagging</title>
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    <abstract>Current supervised parsers are limited by the size of their labelled training data, making improving them with unlabelled data an important goal. We show how a state-of-the-art CCG parser can be enhanced, by predicting lexical categories using unsupervised vector-space embeddings of words. The use of word embeddings enables our model to better generalize from the labelled data, and allows us to accurately assign lexical categories without depending on a POS-tagger. Our approach leads to substantial improvements in dependency parsing results over the standard supervised CCG parser when evaluated on Wall Street Journal (0.8%), Wikipedia (1.8%) and biomedical (3.4%) text. We compare the performance of two recently proposed approaches for classification using a wide variety of word embeddings. We also give a detailed error analysis demonstrating where using embeddings outperforms traditional feature sets, and showing how including POS features can decrease accuracy.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00186</identifier>
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        <date>2014</date>
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%0 Journal Article
%T Improved CCG Parsing with Semi-supervised Supertagging
%A Lewis, Mike
%A Steedman, Mark
%J Transactions of the Association for Computational Linguistics
%D 2014
%V 2
%I MIT Press
%C Cambridge, MA
%F lewis-steedman-2014-improved
%X Current supervised parsers are limited by the size of their labelled training data, making improving them with unlabelled data an important goal. We show how a state-of-the-art CCG parser can be enhanced, by predicting lexical categories using unsupervised vector-space embeddings of words. The use of word embeddings enables our model to better generalize from the labelled data, and allows us to accurately assign lexical categories without depending on a POS-tagger. Our approach leads to substantial improvements in dependency parsing results over the standard supervised CCG parser when evaluated on Wall Street Journal (0.8%), Wikipedia (1.8%) and biomedical (3.4%) text. We compare the performance of two recently proposed approaches for classification using a wide variety of word embeddings. We also give a detailed error analysis demonstrating where using embeddings outperforms traditional feature sets, and showing how including POS features can decrease accuracy.
%R 10.1162/tacl_a_00186
%U https://aclanthology.org/Q14-1026
%U https://doi.org/10.1162/tacl_a_00186
%P 327-338

Markdown (Informal)

[Improved CCG Parsing with Semi-supervised Supertagging](https://aclanthology.org/Q14-1026) (Lewis & Steedman, TACL 2014)

• Improved CCG Parsing with Semi-supervised Supertagging (Lewis & Steedman, TACL 2014)

ACL

• Mike Lewis and Mark Steedman. 2014. Improved CCG Parsing with Semi-supervised Supertagging. Transactions of the Association for Computational Linguistics, 2:327–338.