Algebraic Structures in Natural Language

ISBN-13: 9781032066547 / Angielski / Twarda / 2022 / 304 str.

Opis

Algebraic Structures in Natural Language by bringing together leading researchers from computational and mathematical linguistics, psychology and behavioural science, addresses the learning procedures through which humans acquire natural language, and the way in which they represent its properties.  

Recenzje

“Lappin and Bernardy have assembled a great set of researchers who work on linguistic, cognitive science and natural language processing in deep neural network approaches to language.  The result is a state of the art collection of interest to anyone with interests in DNNs and their connection to human language.” --Edward A. F. Gibson, Professor, MIT Department of Brain & Cognitive Sciences

Spis treści

1. On the Proper Role of Linguistically Oriented Deep Net Analysis in Linguistic Theorizing by Marco Baroni. 2. What Artificial Neural Networks Can Tell Us About Human Language Acquisition by Alex Warstadt and Samuel R. Bowman. 3. Grammar through Spontaneous Order by Nick Chater and Morten H. Christiansen. 4. Language is Acquired in Interaction by Eve V. Clark. 5. Why Algebraic Systems aren’t Sufficient for Syntax by Ben Ambridge. 6. Learning Syntactic Structures from String Input by Ethan Gotlieb Wilcox, Jon Gauthier, Jennifer Hu, Peng Qian, and Roger Levy. 7. Analyzing Discourse Knowledge in Pre-Trained LMs by Sharid Lo´aiciga. 8. Linguistically Guided Multilingual NLP by Olga Majewska, Ivan Vuli´c, and Anna Korhonen. 9. Word Embeddings are Word Story Embeddings (and that’s fine) by Katrin Erk and Gabriella Chronis. 10. Algebra and Language: Reasons for (Dis)content by Lawrence S. Moss. 11. Unitary Recurrent Networks by Jean-Philippe Bernardy and Shalom Lappin.