Seminar für Sprachwissenschaft

Wir haben gerade unseren öffentlichkeitswirksamen Artikel im führenden Fachmagazin "Scientia" veröffentlicht:

Baayen, R. H. (2019). Are You Listening? Teaching a Machine to Understand Speech. Scientia, 2019, 1-5. doi

 

Meine aktuellen zentralen Forschungsinteressen beinhalten das Folgende.

Rechnergestützte Modellierung von lexikalischer Verarbeitung.

Finanziert durch den Europäischen Forschungsgsrat, untersuchen wir das Potential von „Wide Learning“ (Modellierung mit großen linearen Netzwerken), um Prozesse während der lexikalischen Verarbeitung beim Lesen, Hören und Sprechen zu verstehen. Kürzlich haben wir den Wirksamkeitsnachweis erbracht, dass die Verarbeitung von sowohl einfachen, als auch morphologisch komplexen Wörtern, mit hoher Genauigkeit erreicht werden kann, ohne dass es dabei theoretischer Konstrukte wie Morphemen, Stämmen, Exponenten, Inflektionsklassen oder Ausnahmen bedarf.

Unser neues Modell, das „Linear Discriminative Learning“, ist eine Formalisierung von Wort- und Paradigmamorphologie (Blevins, 2016, CUP) die auf diskriminativem Lernern beruht. Eine detaillierte Studie von englischer flektierender und abgeleiteter Morphologie wird in Baayen, Chuang, Shafaei-Bajestan and Blevins (2018, Complexity) bereitgestellt und eine kleine Fallstudie zum Lateinischen steht in Baayen, Chuang und Belvins (2018, The Mental Lexicon) zur Verfügung. Ich bin sowohl angeregt als auch verblüfft darüber, dass die einfachen linearen Mappings, die „Linear Discriminative Learning“ zugrunde liegen, so gut funktionieren.

Phonetik.

In meinem Labor verwenden wir elektromagnetische Artikulographie und Ultraschall, um zu verdeutlichen, wie Sprechende ihren Kiefer und ihre Zunge während der Artikulation verwenden. Wir untersuchten Dialekt geschuldete Unterschiede (Sprecher im Nord-Osten der Niederlande sprechen mit ihrer Zunge im weiter hinteren Teil ihres Mundes, verglichen mit Sprechern im zentralen Osten, Wieling etal. 2016, Journal of Phonetics) und wir haben kürzlich den Nachweis erhalten, dass Übung den Meister macht, auch in Sachen Artikulation (Tomaschek et al., 2018, Linguistic Vanguard). Wir modellieren auch die verschiedenen akustischen Zeitdauern von homophonen Suffixen (z.B. das Englische -s, das an Substantive angehängt den Plural, oder den Genitiv formt und an Verben angehängt die dritte Person Singular ausdrückt) unter Anwendung von „Discriminative Learning“. Statistische Methoden.

Ich hege ein langjähriges Interesse für statistische Methoden, einschließlich „Linear Mixed Effects Models“, „Random Forests“, „Generalized Additive Models“, „Quantile Regression“ und Ereigniszeitanalyse. Besonders beeindruckt bin ich von der Kombination aus „Quantile Regression“ und „Generalized additive Modeling“ als Werkzeug im „qgam package“ für R von Matteo Fasiolo (Universität Bristol). Ich liebe explorative Datenanalyse und habe von den Experimenten am meisten gelernt, die in klarem Widerspruch zu meinen Prognosen standen und unerwartete, neue Trends in meinen Daten offenbarten.

 

Lehre

WS 2021/2022
Linguistics for Cognitive Science

SS 2021
Methods II: Statistics

WS 2020/2021
Linguistics for Cognitive Science

WS 2019/2020
Linguistics for Cognitive Science

SS 2019
Computational Models of Morphological Processing

SS 2019
Introduction to Regression and Data Analysis

WS 2018/2019
Linguisticts for Cognitive Science

SS 2018
Advanced Regression Modeling

SS 2018
Introduction to Regression Modeling

SS 2018
Introduction Linguistics for Cognitive Science

Veröffentlichungen (Auswahl)

Shafaei-Bajestan, E., M. Moradipour-Tari, P. Uhrig, and R. H. Baayen (2021). LDL-AURIS: A computational model, grounded in error-driven learning, for the comprehension of single spoken words. Language, Cognition and Neuroscience, 1-28. Pdf

Shen, T., and R. H. Baayen. (2021). Adjective-Noun Compounds in Mandarin: a Study on Productivity. Corpus Linguistics and Linguistic Theory, 1-30.  Pdf

Tomaschek, F., Tucker, B.V., Ramscar, M., and Baayen, R. H. (2021). Paradigmatic enhancement of stem vowels in regular English inflected verb forms. Morphology, 31, 171-199. Pdf

Baayen, R. H., and Smolka, E. (2020). Modeling morphological priming in German with naive discriminative learning. Frontiers in Communication, section Language Sciences, 1-40. Pdf

Chuang, Y-Y., Bell, M. J., Banke, I., and Baayen, R. H. (2020). Bilingual and multilingual mental lexicon: a modeling study with Linear Discriminative Learning. Language Learning, 1-73. Pdf

Chuang, Y-Y., Vollmer, M-l., Shafaei-Bajestan, E., Gahl, S., Hendrix, P., and Baayen, R. H. (2020). The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using Linear Discriminative Learning. Behavior Research Methods, 1-51. pd

Baayen R.H., Chuang Y., Shafaei-Bajestan E., Blevins J.P. (2019). The discriminative lexicon: A unified computational model for the lexicon and lexical processing in comprehension and production grounded not in (de)composition but in linear discriminative learning. Complexity, 1-39. pdf

Tomaschek, F., Plag, I., Ernestus, M., and Baayen, R. H. (2019). Phonetic effects of morphology and context: Modeling the duration of word-final S in English with naïve discriminative learning. Journal of Linguistics, 1-39. Pdf

Sering K., Milin P., Baayen R. H., (2018). Language comprehension as a multi-label classification problem. Statistica Neerlandica, 72, 339–353. pdf

Tomaschek F., Tucker B.V., Fasiolo M., and Baayen R.H. (2018). Practice makes perfect: The consequences of lexical proficiency for articulation. Linguistics Vanguard, 4, 1-13. pdf

Baayen R.H., Vasishth S., Kliegl R., Bates D. (2017). The cave of shadows. Addressing the human factor with generalized additive mixed models. Journal of Memory and Language, 94:206-234. pdf

Linke M., Bröker F., Ramscar M., Baayen R.H. (2017). Are baboons learning "orthographic" representations? Probably not. PLoS ONE 12(8): e0183876. pdf

Baayen R.H., Shaoul C., Willits J., Ramscar M. (2015). Comprehension without segmentation: A proof of concept with naive discriminative learning. Language, Cognition, and Neuroscience, 31:106–128. pdf

Ramscar M., Baayen R.H. (2014). The myth of cognitive decline: why our minds improve as we age. New Scientist, 221(2961):28–29. pdf

Kösling, K., Kunter, G., Baayen, R. H., and Plag, I. (2013). Prominence in triconstituent compounds: Pitch contours and linguistic theory. Language and Speech, 56, 529–554. Pdf

Baayen R.H., Milin P., Durdević D. F., Hendrix P., Marelli M. (2011). An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. Psychological Review, 118:438–482. pdf

Baayen R.H. (2008). Analyzing linguistic data: A practical introduction to statics using R. Cambridge University Press. pdf

Baayen, R.H. (2001). Word Frequency Distributions, Kluwer Academic Publishers. pdf

Präsentationen der letzten 2 Jahre

2022

Baayen, R. H., Chuang, Y.-Y., Hsieh, S.-K., Tseng, S., Chen, J., and Shen, T., Conceptualising for compounding: Mandarin two-syllable compounds and names, Workshop on Morphology and Word Embeddings, Tübingen, Germany, January 18, 2022 (virtual talk).

2021

Baayen, R. H., Explorations into gesture, 2021 International Conference on Multimodal Communication: Emerging Computational and Technical Methods (ICMC2021), Changsha, China, December 11, 2021 (virtual talk).\

Heitmeier, M., Chuang, Y.-Y., and Baayen, R. H., Modeling German nonword plural productions with Linear Discriminative Learning, Words in the World 2021, Montreal, Canada, November 26, 2021 (virtual poster presentation).

Shafaei-Bajestan, E., Moradipour-Tari, M., Uhrig , P., and Baayen, R. H., Inflectional analogies with word embeddings: there is more than the average, Words in the World 2021, Montreal, Canada, November 26, 2021 (virtual talk).

Baayen, R. H., and Chuang, Y.-Y., Modeling morphology with multivariate multiple regression, Workshop Recent Approaches to the Quantitative Study of Language: Rules and Un-rules, Neuchatel, Switzerland, October 14, 2021 (virtual talk).

Shen, T., and Baayen, R. H., Productivity and semantic transparency: An exploration of compounding in Mandarin, Workshop Perspectives on productivity, Leuven, Belgium, May 26, 2021 (virtual talk)

Baayen, R. H., and Gahl, S., Thyme and time again: Semantics all the way down, Internal Workshop FOR2373, Düsseldorf, Germany, March 18, 2021 (virtual talk).

Luo, X., Chuang, Y-Y., and Baayen, R. H., Linear Discriminative Learning in Julia, International Conference on Error-Driven Learning in Language (EDLL 2021), Tübingen, Germany, March 11, 2021 (virtual poster presentation).

2020

Sering, K., Schmidt-Barbo, P., Otte, S., Butz, M. V., and Harald Baayen, R. H., Recurrent Gradient-based Motor Inference for Speech Resynthesis with a Vocal Tract Simulator, the 12th International Seminar on Speech Production (ISSP 2020), New Haven, USA, December 14, 2020 (virtual poster presentation).

Baayen, R. H., A multivariate multiple regression approach to the mental lexicon, the 28th International Conference on Computational Linguistics (COLING’2020), Barcelona, Spain, December 8, 2020 (virtual invited talk).

Baayen, R. H., Quantitative Cognitive Linguistics, Hindustan Institute of Technology & Science, Chennai, India, November 23, 2020 (virtual talk)

Baayen, R. H., A discriminative perspective on learning a new language, 7th International Scientific Interdisciplinary Conference on Research and Methodology, Moscow, Russia, November 20, 2020 (virtual keynote).

Baayen, R. H., and Chuang, Y.-Y., How long you make your words crucially depends on their meanings, PACLIC 2020 - The 34th Pacific Asia Conference on Language, Information and Computation, Hanoi, Vietnam, October 24, 2020 (virtual talk).

Li, J., Chuang, Y.-Y., and Baayen, R. H., Tonal (ir)regularity and word frequency in Mandarin bisyllabic compounds, Words in the World International Conference 2020, St. Catharines, Canada, October 18, 2020 (virtual talk).

Saito, M., Tomaschek, F., and Baayen, R. H., Co-articulation between stem vowels and suffixes: semantics all the way down, Words in the World International Conference 2020, St. Catharines, Canada, October 18, 2020 (virtual talk).

Luo, X., Chuang, Y.-Y., and Baayen, R. H., Implementation of Linear Discriminative Learning in Julia, Words in the World International Conference 2020, St.Catharines, Canada, October 16, 2020 (virtual talk).

Baayen, R. H., Chuang, Y.-Y., and Shafaei-Bajestan, E., Using discriminative learning to model comprehension and production of inflectional morphology without morphemes and without inflectional classes, Workshop How to fill a cell: computational approaches to inflectional morphology, Sheffield, United Kingdom, September 16, 2020 (virtual talk).

Baayen, R. H., A blueprint for discriminative learning of simple utterances, TÜling Linguistics Lectures, Tartu, Estonia, May 5, 2020 (virtual talk).

Chuang, Y.-Y., Lõo, K., Blevins, J. P., and Baayen, R. H., Estonian case inflection made simple. A case study in word and paradigm morphology with linear discriminative learning, IMM19/PsyComMT, Vienna, Austria, February 7, 2020.

Heitmeier, M., and Baayen, R. H., Simulating phonological and semantic impairment of English tense inflection with linear discriminative Learning, IMM19/PsyComMT, Vienna, Austria, February 7, 2020.

Shafaei-Bajestan, E., and Baayen, R. H., Wide learning of the comprehension of morphologically complex words: from audio signal to semantics, IMM19/PsyComMT, Vienna, Austria, February 7, 2020.