Deriving lexical and syntactic expectation-based measures for psycholinguistic modeling via incremental top-down parsing

Brian Roark; Asaf Bachrach; Carlos Cardenas; Christophe Pallier

doi:10.3115/1699510.1699553

Deriving lexical and syntactic expectation-based measures for psycholinguistic modeling via incremental top-down parsing

Brian Roark
, Asaf Bachrach
, Carlos Cardenas
, Christophe Pallier

Center for Spoken Language Understanding
Oregon Health & Science University
Service Hospitalier Frédéric Joliot
Massachusetts Institute of Technology

Résultats de recherche: Contribution à une conférence › Papier › Revue par des pairs

155 Citations (Scopus)

Résumé

A number of recent publications have made use of the incremental output of stochastic parsers to derive measures of high utility for psycholinguistic modeling, following the work of Hale (2001; 2003; 2006). In this paper, we present novel methods for calculating separate lexical and syntactic surprisal measures from a single incremental parser using a lexicalized PCFG. We also present an approximation to entropy measures that would otherwise be intractable to calculate for a grammar of that size. Empirical results demonstrate the utility of our methods in predicting human reading times.

langue originale	Anglais
Pages	324-333
Nombre de pages	10
Les DOIs	https://doi.org/10.3115/1699510.1699553
état	Publié - 1 janv. 2009
Modification externe	Oui
Evénement	2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009 - Singapore, Singapour Durée: 6 août 2009 → 7 août 2009

Une conférence

Une conférence	2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009
Pays/Territoire	Singapour
La ville	Singapore
période	6/08/09 → 7/08/09

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10.3115/1699510.1699553

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Roark, B., Bachrach, A., Cardenas, C., & Pallier, C. (2009). Deriving lexical and syntactic expectation-based measures for psycholinguistic modeling via incremental top-down parsing. 324-333. Papier présenté � 2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009, Singapore, Singapour. https://doi.org/10.3115/1699510.1699553

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abstract = "A number of recent publications have made use of the incremental output of stochastic parsers to derive measures of high utility for psycholinguistic modeling, following the work of Hale (2001; 2003; 2006). In this paper, we present novel methods for calculating separate lexical and syntactic surprisal measures from a single incremental parser using a lexicalized PCFG. We also present an approximation to entropy measures that would otherwise be intractable to calculate for a grammar of that size. Empirical results demonstrate the utility of our methods in predicting human reading times.",

author = "Brian Roark and Asaf Bachrach and Carlos Cardenas and Christophe Pallier",

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doi = "10.3115/1699510.1699553",

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Roark, B, Bachrach, A, Cardenas, C & Pallier, C 2009, 'Deriving lexical and syntactic expectation-based measures for psycholinguistic modeling via incremental top-down parsing', Papier présenté � 2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009, Singapore, Singapour, 6/08/09 - 7/08/09 p. 324-333. https://doi.org/10.3115/1699510.1699553

Deriving lexical and syntactic expectation-based measures for psycholinguistic modeling via incremental top-down parsing. / Roark, Brian; Bachrach, Asaf; Cardenas, Carlos et al.
2009. 324-333 Papier présenté � 2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009, Singapore, Singapour.

Résultats de recherche: Contribution à une conférence › Papier › Revue par des pairs

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AB - A number of recent publications have made use of the incremental output of stochastic parsers to derive measures of high utility for psycholinguistic modeling, following the work of Hale (2001; 2003; 2006). In this paper, we present novel methods for calculating separate lexical and syntactic surprisal measures from a single incremental parser using a lexicalized PCFG. We also present an approximation to entropy measures that would otherwise be intractable to calculate for a grammar of that size. Empirical results demonstrate the utility of our methods in predicting human reading times.

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Deriving lexical and syntactic expectation-based measures for psycholinguistic modeling via incremental top-down parsing

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