2026-05-13T04:07:04Zhttps://iris.cnr.it/oai/requestoai:iris.cnr.it:20.500.14243/4153892024-12-07T04:03:01Zcom_20.500.14243_46com_20.500.14243_21col_20.500.14243_48ou_ou239
An information-theoretic analysis of the inflectional regular-irregular gradient for optimal processing units
Marzi C
Pirrelli V
Marzi, C
Pirrelli, V
Morphological inflection
prediction-driven processing
discriminability
non-linearity
learnability
Prediction-driven word processing defines the human ability to anticipate upcoming input words in recognition. From this perspective, input word forms need to be processed as quickly and efficiently as possible. Under the reasonable assumption that spoken words are memorized and processed as word trees (e.g. Marslen-Wilson's "cohorts"), the larger the size of the cohort of an input word at a certain point in time (and the later its uniqueness point), the harder and slower to process the word is. Regularly and irregularly inflected verb forms have different stem family sizes and different uniqueness points. Using a Recurrent Neural Network (RNN) as a computational model of the human lexical proces- sor, we explore here how their distributional and structural properties may affect (optimal) processing strategies.
2022
info:eu-repo/semantics/conferenceObject
https://hdl.handle.net/20.500.14243/415389
https://archive.nytud.hu/imm20/abstracts/main.pdf
eng
ispartofbook:Book of Abstracts of the 20th International Morphology Meeting - Dedicated to the memory of Ferenc Kiefer
20th International Morphology Meeting - (Dedicated to the memory of Ferenc Kiefer)
firstpage:50
lastpage:51
numberofpages:2
info:eu-repo/semantics/openAccess
ELETTRONICO
license:Altro tipo di licenza
license:Altro tipo di licenza
license uri:iris.PUB01
license uri:iris.PUB01