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DTSTART;TZID=Europe/Paris:20161202T110000
DTEND;TZID=Europe/Paris:20161202T130000
DTSTAMP:20260423T025012
CREATED:20190212T171914Z
LAST-MODIFIED:20190212T171917Z
UID:2261-1480676400-1480683600@www.ilcb.fr
SUMMARY:Man and Machine during Natural Language Processing: A Neurocognitive Approach by Chris Biemann and Markus J. Hofmann
DESCRIPTION:Man and Machine during Natural Language Processing: A Neurocognitive Approach by Chris Biemann and Markus J. Hofmann Language Technology\, Universität Hamburg General and Biological Psychology\, University of Wuppertal\nWhile state-of-the-art NLP models lack a theory that systematically accounts for human performance at all levels of linguistic analysis\, Neurocognitive Simulation Models of orthographic and phonological memory so far lacked a level of implemented semantic representations. To overcome these limitations\, the authors of this talk decided to initiate a long-standing cooperation.\n\nIn part 1 of this talk\, we introduce unsupervised methods from language technology that capture semantic information. We present a range of methods that extract semantic representation from corpora\, as opposed to using manually created norms. We show how we applied language models based on n-grams\, topic modelling\, and the word2vec neural model across three different corpora to account for behavioral\, brain-electric and eye movement data. We used a benchmark that has become standard for Neurocognitive Simulation Models in psychology: Thus we reproducibly accounted for half of the item-level variance in the cloze-completion-based word predictability from sentence context\, and the resulting N400-\, and single fixation duration data of the Potsdam sentence corpus.\n\nIn part 2 we discuss how relatively straightforward NLP methods can be used to define semantic processes in a neurocognitive simulation model. To extend an interactive activation model with a semantic layer\, we used the log likelihood that two words occur more often together in the sentences of a large corpus than predictable by single-word frequency. The resulting Associative Read-Out Model (AROM) is an extension of the Multiple Read-Out Model. Here\, we use it to account for association ratings and semantically induced false memories in human performance and P200/N400 brain-electric data. Then\, we present a sequential version of the AROM accounting for primed lexical decision\, and the resulting semantic competition in the left (and right!) inferior frontal gyrus of the human brain. Finally\, we envision two routes of reading\, complementing the form-based aspects of linguistic representations with one of the most defining feature of words: they carry meaning.
URL:https://www.ilcb.fr/event/man-and-machine-during-natural-language-processing-a-neurocognitive-approach-by-chris-biemann-and-markus-j-hofmann/
LOCATION:Salle des voûtes\, St Charles\, 3 place Victor Hugo\, Marseille\, 13001\, France
CATEGORIES:Seminars
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DTSTART;TZID=Europe/Paris:20161209T110000
DTEND;TZID=Europe/Paris:20161209T130000
DTSTAMP:20260423T025012
CREATED:20190212T171652Z
LAST-MODIFIED:20190212T171654Z
UID:2259-1481281200-1481288400@www.ilcb.fr
SUMMARY:Figures of speech in the brain: The role of metaphoricity\, familiarity\, concreteness\, and lateralization in language comprehension by Bálint Forgács
DESCRIPTION:Figures of speech in the brain: The role of metaphoricity\, familiarity\, concreteness\, and lateralization in language comprehension by Bálint Forgács (Laboratoire Psychologie de la Perception (LPP) Université Paris Descartes)\nDebates are hot regarding how metaphors are related to literal language\, in what steps we understand them\, and how our brains deal with them. In my talk I am going to show fMRI and divided visual half field data arguing against a unique role for the right cerebral hemisphere and literal language in metaphor comprehension. If the relevant psycholinguistic factors are controlled for (such as context\, emotional valence or imageability) classical left lateralized regions seem to compute not just dead\, but even novel metaphors. Moreover\, the latter do not seem to evoke the so called electrophysiological concreteness effect either\, contrary to the claims of the strong version of embodiment. Based on the new evidence I am going to present a novel model of how the neural systems dedicated to language could compute figures of speech so swiftly and quickly\, and why the lateralization debate could be viewed from a different perspective.
URL:https://www.ilcb.fr/event/figures-of-speech-in-the-brain-the-role-of-metaphoricity-familiarity-concreteness-and-lateralization-in-language-comprehension-by-balint-forgacs/
LOCATION:Salle de conférences\, 5 avenue Pasteur\, Aix-en-Provence\, 13100\, France
CATEGORIES:Seminars
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