Incremental, Predictive Parsing with Psycholinguistically Motivated Tree-Adjoining Grammar

Demberg, Vera; Keller, Frank; Koller, Alexander

doi:10.1162/coli_a_00160

Cited by 64 publications

(76 citation statements)

References 41 publications

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“…An obvious question is why we did not observe surprisal effects in the eyetracking measures given past reports of surprisal effects on reading time (Boston et al, 2008;Demberg and Keller, 2008;Demberg et al, 2013;Levy, 2008). It is important to note, however, that these earlier studies investigated either overall surprisal (i.e., a combination of n-gram and lexicalized syntactic surprisal) or un-lexicalized part-of-speech (POS) surprisal, both of which are different from the surprisal measure we investigated.…”

Section: Discussionmentioning

confidence: 85%

“…Higher surprisal values are associated with greater processing difficulty: Surprisal values reliably predict word-by-word reading times derived from eye movement measures (e.g., Boston et al, 2008;Demberg and Keller, 2008;Demberg et al, 2013;Levy, 2008), the N400 event-related potential (ERP) component (Frank et al, 2015), and fMRI activation (Hale et al, 2015;Willems et al, in press).…”

Section: Introductionmentioning

confidence: 99%

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Language structure in the brain: A fixation-related fMRI study of syntactic surprisal in reading

et al. 2016

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How is syntactic analysis implemented by the human brain during language comprehension? The current study combined methods from computational linguistics, eyetracking, and fMRI to address this question. Subjects read passages of text presented as paragraphs while their eye movements were recorded in an MRI scanner. We parsed the text using a probabilistic context-free grammar to isolate syntactic difficulty. Syntactic difficulty was quantified as syntactic surprisal, which is related to the expectedness of a given word's syntactic category given its preceding context. We compared words with high and low syntactic surprisal values that were equated for length, frequency, and lexical surprisal, and used fixation-related (FIRE) fMRI to measure neural activity associated with syntactic surprisal for each fixated word. We observed greater neural activity for high than low syntactic surprisal in two predicted cortical regions previously identified with syntax: left inferior frontal gyrus (IFG) and less robustly, left anterior superior temporal lobe (ATL). These results support the hypothesis that left IFG and ATL play a central role in syntactic analysis during language comprehension. More generally, the results suggest a broader cortical network associated with syntactic prediction that includes increased activity in bilateral IFG and insula, as well as fusiform and right lingual gyri.© 2016 Elsevier Inc. All rights reserved. Keywords:Language Syntax Surprisal Reading Eye movements fMRI IntroductionA complete theory of the human brain must include a description of the neural networks responsible for language processing. Comprehension of connected sentences requires the ability to retrieve words from the lexicon and relate those words to each other, as the input is encountered in real time. The system for creating these groupings is known as syntax, and is generally thought of as a set of computations specifying syntactic categories such as noun and verb, and stating how those categories combine into increasingly larger constituents such as noun phrases, verb phrases, and clauses. The constituent structure of a sentence is the frame on which interpretations are built; the same words will have radically different meanings depending on that structure (e.g., the dog bit the man vs. the man bit the dog). Thus, the human ability to understand language is based both on lexical knowledge and syntactic computations.In this study, we focused on understanding how the human brain implements the syntactic component of the human language faculty. Specifically, we were interested in the nature of the cortical systems that compute syntactic representations during online language comprehension. Three regions have traditionally been identified as candidates for syntactic processing (Fedorenko et al., 2012;Friederici and Kotz, 2003;Grodzinsky and Friederici, 2006;Kaan and Swaab, 2002). Historically, Broca's area or left inferior frontal gyrus (IFG) has been most associated with syntax (Ben-Shachar et al., 2003;Ben-Shachar et al., 2004;...

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Section: Discussionmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Language structure in the brain: A fixation-related fMRI study of syntactic surprisal in reading

et al. 2016

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“…The current work builds on an existing incremental parser, the Psycholinguistically Motivated Tree Adjoining Grammar (PLTAG) parser of Demberg et al (2013). The distinguishing feature of this parser is that it builds fully connected structures (no words are left unattached during incremental parsing); this requires it to make predictions about the right context, which are verified as more of the input becomes available.…”

Section: Incremental Semantic Role Labelingmentioning

confidence: 99%

“…This ensures that fully connected prefix trees can be built for every prefix of the input. In order to efficiently parse PLTAG, Demberg et al (2013) introduce the concept of fringes. Fringes capture the fact that in an incremental derivation, a prefix tree can only be combined with an elementary tree at a limited set of nodes.…”

Section: Psycholinguistically Motivated Tagmentioning

confidence: 99%

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Semantic Role Labeling Improves Incremental Parsing

Konstas¹,

Keller²

2015

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Confere

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Incremental parsing is the task of assigning a syntactic structure to an input sentence as it unfolds word by word. Incremental parsing is more difficult than fullsentence parsing, as incomplete input increases ambiguity. Intuitively, an incremental parser that has access to semantic information should be able to reduce ambiguity by ruling out semantically implausible analyses, even for incomplete input. In this paper, we test this hypothesis by combining an incremental TAG parser with an incremental semantic role labeler in a discriminative framework. We show a substantial improvement in parsing performance compared to the baseline parser, both in full-sentence F-score and in incremental F-score.

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The temporal dynamics of structure and content in sentence comprehension: Evidence from fMRI‐constrained MEG

Matchin

Brodbeck

Hammerly

et al. 2018

Human Brain Mapping

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Humans have a striking capacity to combine words into sentences that express new meanings. Previous research has identified key brain regions involved in this capacity, but little is known about the time course of activity in these regions, as hemodynamic methods such as fMRI provide little insight into temporal dynamics of neural activation. We performed an MEG experiment to elucidate the temporal dynamics of structure and content processing within four brain regions implicated by fMRI data from the same experiment: the temporo‐parietal junction (TPJ), the posterior temporal lobe (PTL), the anterior temporal lobe (ATL), and the anterior inferior frontal gyrus (IFG). The TPJ showed increased activity for both structure and content near the end of the sentence, consistent with a role in incremental interpretation of event semantics. The PTL, a region not often associated with core aspects of syntax, showed a strong early effect of structure, consistent with predictive parsing models, and both structural and semantic context effects on function words. These results provide converging evidence that the PTL plays an important role in lexicalized syntactic processing. The ATL and IFG, regions traditionally associated with syntax, showed minimal effects of sentence structure. The ATL, PTL and IFG all showed effects of semantic content: increased activation for real words relative to nonwords. Our fMRI‐guided MEG investigation therefore helps identify syntactic and semantic aspects of sentence comprehension in the brain in both spatial and temporal dimensions.

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Incremental, Predictive Parsing with Psycholinguistically Motivated Tree-Adjoining Grammar

Cited by 64 publications

References 41 publications

Language structure in the brain: A fixation-related fMRI study of syntactic surprisal in reading

Language structure in the brain: A fixation-related fMRI study of syntactic surprisal in reading

Semantic Role Labeling Improves Incremental Parsing

The temporal dynamics of structure and content in sentence comprehension: Evidence from fMRI‐constrained MEG

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