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Retrieval (N400) and integration (P600) in expectation-based comprehension
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In: PLoS One (2021)
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Neurobehavioral Correlates of Surprisal in Language Comprehension: A Neurocomputational Model
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In: Front Psychol (2021)
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The online processing of causal and concessive discourse connectives
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Rational Redundancy in Referring Expressions: Evidence from Event‐related Potentials
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Splitting event-related potentials: Modeling latent components using regression-based waveform estimation
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The influence of lexical priming versus event knowledge on the N400 and the P600 ...
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On the predictability of event boundaries in discourse: An ERP investigation
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The ARRAU Corpus of Anaphoric Information
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Abstract:
*Introduction* The ARRAU (Anaphora Resolution and Underspecification) Corpus of Anaphoric Information was developed by the University of Essex and the University of Trento. It contains annotations of multi-genre English texts for anaphoric relations with information about agreement and explicit representation of multiple antecedents for ambiguous anaphoric expressions and discourse antecedents for expressions which refer to abstract entities such as events, actions and plans. The source texts in this release include task-oriented dialogues from the TRAINS-91 and TRAINS-93 corpora (the latter released through LDC, TRAINS Spoken Dialog Corpus LDC95S25), narratives from the English Pear Stories (a collection of narratives by subjects who watched a film and then recounted its contents), articles from the Wall Street Journal portions of the Penn Treebank (Treebank-2 LDC95T7) and the RST Discourse Treebank LDC2002T07, and the Vieira/Poesio Corpus which consists of training and test files from Treebank-2 and RST Discourse Treebank. *Data* The texts were annotated using the ARRAU guidelines which treat all noun phrases (NPs) as markables. Different semantic roles are recognized by distinguishing between referring expressions (that update or refer to a discourse model), and non-referring ones (including expletives, predicative expressions, quantifiers, and coordination). A variety of linguistic features were also annotated, including morphosyntactic agreement, grammatical function, semantic type (person, animate, concrete, action, time, other abstract) and genericity. The annotation was carried out using the MMAX2 annotation tool which allows text units to be marked at different levels. The files in MMAX format have been organized so that they can be visualized using the MMAX2 tool or directly used as input/output for the BART toolkit which performs automatic coreference resolution including all necessary preprocessing steps. *Samples* Please view the following samplesL * Raw * PRD * Header *Updates* None at this time.
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URL: https://catalog.ldc.upenn.edu/LDC2013T22
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