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X-WR-CALDESC:DeustoCCM - Chair of Computational Mathematics at University of Deusto
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DTSTART:20241113T133000Z
DTEND:20241113T153000Z
DTSTAMP:20251031T222700Z
CREATED:20251031
LAST-MODIFIED:20251031
PRIORITY:5
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SUMMARY:FAU MoD Lecture: Measuring productivity and fixedness in lexico-syntactic constructions
DESCRIPTION:Date: Wed. November 13, 2024\nEvent: FAU MoD Lecture\nOrganized by: FAU MoD, the Research Center for Mathematics of Data at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)\nFAU MoD Lecture: Measuring productivity and fixedness in lexico-syntactic constructions\nSpeaker: Prof. Dr. Stephanie Evert\nAffiliation: FAU MoD member/vice-spokesperson | CCL – Chair of Computational Corpus Linguistics. Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)\nAbstract. In cognitive linguistics, constructions are understood as pairings of form (i.e. a lexico-grammatical pattern) and meaning (as a parameterised function if the pattern contains variable elements), which constitute the fundamental building blocks of speakers’ linguistic knowledge. Between the extremes of purely syntactic constructions (such as the ditransitive) and purely lexical ones (individual words or multiword units), a large part of constructions fall somewhere in the middle of the lexis-grammar continuum. They often consist of multiple lexical and grammatical elements, which range from completely fixed lexical items to highly variable slots.\nIn this talk I argue that the variability of slots in a lexico-grammatical pattern forms a cline ranging from complete fixedness to full productivity. This cline cannot be quantified by a single integrated measure, but is a combination of three distinct, but overlapping aspects:\n(i) fixedness is quantified by the frequency of an element (or rather, its conditional probability given the other items in the lexico-grammatical pattern);\n(ii) at the opposite end of the cline, productivity is quantified by type-token measures and interpreted with the help of statistical LNRE models;\n(iii) in the middle ground between productivity and fixedness, statistical association plays a central role in identifying salient, semi-fixed lexical items.\nThese methodological considerations are illustrated with a case study on shell noun constructions such as “It is a fact that you will have to listen to the entire talk.”\n\nSee poster\nBIO.- Prof. Dr. Stephanie Evert is a Professor at the Chair of Computational Corpus Linguistics at Friedrich-Alexander-Universität Erlangen-Nürnberg. After studying mathematics, physics and English linguistics, she received a PhD degree in computational linguistics from the University of Stuttgart, Germany. Her research interests encompass the quantitative methodology of corpus linguistics, multivariate analysis and distributional semantics, applied corpus studies and digital humanities, tools for processing large text corpora, the combination of human interpretation with machine learning (digital hermeneutics), as well as language technology and its applications.\nProf. Evert is member of the Steering Committee and Vice-spokesperson of our FAU MoD.\nAUDIENCE\nThis is a hybrid event (On-site/online) open to: Public, Students, Postdocs, Professors, Faculty, Alumni and the scientific community all around the world.\nWHEN\nWed. November 13, 2024 at 14:30H (Berlin time)\nWHERE\nOn-site / Online\n[On-site]\nFriedrich-Alexander-Universität Erlangen-Nürnberg\nRoom H13 Johann-Radon-Hörsaal\nCauerstraße 11, 91058 Erlangen\nGPS-Koord. Raum: 49.573764N, 11.030028E\n[Online]\nFAU Zoom link\nMeeting ID: 680 1463 6900 | PIN code: 222990\nThis event on LinkedIn\n_\n* Photo by Glasow\n \nYou might like:\n• FAU MoD Lectures\n• FAU MoD Lecture: New avenues for the interaction of computational mechanics and machine learning by Prof. Dr. Paolo Zunino\n• FAU MoD Lecture: Discovering and Communicating Excellence by Prof. Dr. Ute Klammer\n• FAU MoD Lecture: Thoughts on Machine Learning by Prof. Dr. Rupert Klein\n• FAU MoD Lecture: Using system knowledge for improved sample efficiency in data-driven modeling and control of complex technical systems by Prof. Dr. Sebastian Peitz\n• FAU MoD Lecture: Image Reconstruction – The Dialectic of Modelling and Learning by Prof. Dr. Martin Burger\n• FAU MoD Lecture: The role of Artificial Intelligence in the future of mathematics by Prof. Dr. Amaury Hayat\n• FAU MoD Lecture: FAU MoD Lecture. Special November 2023 by Prof. Dr. Michael Kohlhase and Prof. Dr. Edriss S. Titi\n• FAU MoD Lecture: Free boundary regularity for the obstacle problem by Prof. Dr. Alessio Figalli\n• FAU MoD Lecture: Physics-Based and Data-Driven-Based Algorithms for the Simulation of the Heart Function  by Prof. Dr. Alfio Quarteroni\n• FAU MoD Lecture: From Physics-Informed Machine Learning to Physics-Informed Machine Intelligence: Quo Vadimus?  by Prof. Dr. George Karniadakis\n• FAU MoD Lecture: From Alan Turing to contact geometry: Towards a “Fluid computer” by Prof. Dr. Eva Miranda\n• FAU MoD Lecture:  Applications of AAA Rational Approximation by Prof. Dr. Nick Trefethen\n• FAU MoD Lecture:  Learning-Based Optimization and PDE Control in User-Assignable Finite Time by Prof. Dr. Miroslav Krstic\n \n_\nDon’t miss out our last news and connect with us!\nwww.mod.fau.eu/events ( http://www.mod.fau.eu/events )\nLinkedIn | X (Twitter) | Instagram\n
URL:https://cmc.deusto.eus/events-calendar/fau-mod-lecture-measuring-productivity-and-fixedness-in-lexico-syntactic-constructions/
ORGANIZER;CN=FAU MoD:MAILTO:
CATEGORIES:FAU MoD Lecture,Seminar/Talk
LOCATION:Worldwide
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