Surprising parser actions and reading difficulty

Boston, Marisa Ferrara; Hale, John; Kliegl, Reinhold; Vasishth, Shravan

doi:10.3115/1557690.1557693

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Formal measures of word information have only been proposed with respect to generative language models, in particular Probabilistic Context-Free Grammars. [1][2][3][6][7][8][9][10] By definition, a generative language model defines a probability distribution over sentences. As the words of a sentence are processed one at a time, this distribution changes.…”

Section: Measuring Word Informationmentioning

confidence: 99%

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Sentence Comprehension as Mental Simulation: An Information-Theoretic Analysis and a Connectionist Model

Frank

2011

Connectionist Models of Neurocognition and Emergent Behavior

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It has been argued that understanding a sentence comes down to mentally simulating the state-of-affairs described by the sentence. This paper presents a particular formalization of this idea and shows how it gives rise to measures of the amount of syntactic and semantic information conveyed by each word in a sentence. These information measures predict simulated word-processing times in a connectionist model of sentence comprehension.

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Section: Measuring Word Informationmentioning

confidence: 99%

“…1,3 Indeed, reading times have repeatedly been shown to correlate positively with surprisal as estimated by a range of language models. [7][8][9]11,12 2.1.…”

Section: Syntactic Surprisalmentioning

confidence: 99%

Sentence Comprehension as Mental Simulation: An Information-Theoretic Analysis and a Connectionist Model

Frank

2011

Connectionist Models of Neurocognition and Emergent Behavior

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

Roark¹,

Bachrach²,

Cárdenas³

et al. 2009

Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing Volume 1 - EMNLP '09

136

194

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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;. 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.

show abstract

Surprising parser actions and reading difficulty

Cited by 2 publications

References 18 publications

Sentence Comprehension as Mental Simulation: An Information-Theoretic Analysis and a Connectionist Model

Sentence Comprehension as Mental Simulation: An Information-Theoretic Analysis and a Connectionist Model

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

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