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        <title>Coarse Lexical Frame Acquisition at the Syntax–Semantics Interface Using a Latent-Variable PCFG Model</title>
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        <namePart type="given">Laura</namePart>
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        <namePart type="given">Behrang</namePart>
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            <namePart type="family">Nissim</namePart>
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    <abstract>We present a method for unsupervised lexical frame acquisition at the syntax–semantics interface. Given a set of input strings derived from dependency parses, our method generates a set of clusters that resemble lexical frame structures. Our work is motivated not only by its practical applications (e.g., to build, or expand the coverage of lexical frame databases), but also to gain linguistic insight into frame structures with respect to lexical distributions in relation to grammatical structures. We model our task using a hierarchical Bayesian network and employ tools and methods from latent variable probabilistic context free grammars (L-PCFGs) for statistical inference and parameter fitting, for which we propose a new split and merge procedure. We show that our model outperforms several baselines on a portion of the Wall Street Journal sentences that we have newly annotated for evaluation purposes.</abstract>
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    <identifier type="doi">10.18653/v1/S18-2016</identifier>
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%0 Conference Proceedings
%T Coarse Lexical Frame Acquisition at the Syntax–Semantics Interface Using a Latent-Variable PCFG Model
%A Kallmeyer, Laura
%A QasemiZadeh, Behrang
%A Cheung, Jackie Chi Kit
%Y Nissim, Malvina
%Y Berant, Jonathan
%Y Lenci, Alessandro
%S Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F kallmeyer-etal-2018-coarse
%X We present a method for unsupervised lexical frame acquisition at the syntax–semantics interface. Given a set of input strings derived from dependency parses, our method generates a set of clusters that resemble lexical frame structures. Our work is motivated not only by its practical applications (e.g., to build, or expand the coverage of lexical frame databases), but also to gain linguistic insight into frame structures with respect to lexical distributions in relation to grammatical structures. We model our task using a hierarchical Bayesian network and employ tools and methods from latent variable probabilistic context free grammars (L-PCFGs) for statistical inference and parameter fitting, for which we propose a new split and merge procedure. We show that our model outperforms several baselines on a portion of the Wall Street Journal sentences that we have newly annotated for evaluation purposes.
%R 10.18653/v1/S18-2016
%U https://aclanthology.org/S18-2016
%U https://doi.org/10.18653/v1/S18-2016
%P 130-141

Markdown (Informal)

[Coarse Lexical Frame Acquisition at the Syntax–Semantics Interface Using a Latent-Variable PCFG Model](https://aclanthology.org/S18-2016) (Kallmeyer et al., *SEM 2018)

• Coarse Lexical Frame Acquisition at the Syntax–Semantics Interface Using a Latent-Variable PCFG Model (Kallmeyer et al., *SEM 2018)

ACL

• Laura Kallmeyer, Behrang QasemiZadeh, and Jackie Chi Kit Cheung. 2018. Coarse Lexical Frame Acquisition at the Syntax–Semantics Interface Using a Latent-Variable PCFG Model. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, pages 130–141, New Orleans, Louisiana. Association for Computational Linguistics.