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        <title>Semi-Supervised Lexicon Learning for Wide-Coverage Semantic Parsing</title>
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    <name type="personal">
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    <abstract>Semantic parsers critically rely on accurate and high-coverage lexicons. However, traditional semantic parsers usually utilize annotated logical forms to learn the lexicon, which often suffer from the lexicon coverage problem. In this paper, we propose a graph-based semi-supervised learning framework that makes use of large text corpora and lexical resources. This framework first constructs a graph with a phrase similarity model learned by utilizing many text corpora and lexical resources. Next, graph propagation algorithm identifies the label distribution of unlabeled phrases from labeled ones. We evaluate our approach on two benchmarks: Webquestions and Free917. The results show that, in both datasets, our method achieves substantial improvement when comparing to the base system that does not utilize the learned lexicon, and gains competitive results when comparing to state-of-the-art systems.</abstract>
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%0 Conference Proceedings
%T Semi-Supervised Lexicon Learning for Wide-Coverage Semantic Parsing
%A Chen, Bo
%A An, Bo
%A Sun, Le
%A Han, Xianpei
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F chen-etal-2018-semi
%X Semantic parsers critically rely on accurate and high-coverage lexicons. However, traditional semantic parsers usually utilize annotated logical forms to learn the lexicon, which often suffer from the lexicon coverage problem. In this paper, we propose a graph-based semi-supervised learning framework that makes use of large text corpora and lexical resources. This framework first constructs a graph with a phrase similarity model learned by utilizing many text corpora and lexical resources. Next, graph propagation algorithm identifies the label distribution of unlabeled phrases from labeled ones. We evaluate our approach on two benchmarks: Webquestions and Free917. The results show that, in both datasets, our method achieves substantial improvement when comparing to the base system that does not utilize the learned lexicon, and gains competitive results when comparing to state-of-the-art systems.
%U https://aclanthology.org/C18-1076
%P 892-904

Markdown (Informal)

[Semi-Supervised Lexicon Learning for Wide-Coverage Semantic Parsing](https://aclanthology.org/C18-1076) (Chen et al., COLING 2018)

• Semi-Supervised Lexicon Learning for Wide-Coverage Semantic Parsing (Chen et al., COLING 2018)

ACL

• Bo Chen, Bo An, Le Sun, and Xianpei Han. 2018. Semi-Supervised Lexicon Learning for Wide-Coverage Semantic Parsing. In Proceedings of the 27th International Conference on Computational Linguistics, pages 892–904, Santa Fe, New Mexico, USA. Association for Computational Linguistics.