Abstract in atti di convegno, 2022, ENG
Marzi C., Pirrelli V.
Istituto di Linguistica Computazionale - CNR
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.
20th International Morphology Meeting - (Dedicated to the memory of Ferenc Kiefer), pp. 50–51, Budapest, 01-04/09/2022
Morphological inflection, prediction-driven processing, discriminability, non-linearity, learnability
ILC – Istituto di linguistica computazionale "Antonio Zampolli"
ID: 471259
Year: 2022
Type: Abstract in atti di convegno
Creation: 2022-09-26 17:03:18.000
Last update: 2023-12-22 11:05:25.000
CNR authors
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:471259