Abstract in atti di convegno, 2022, ENG

An information-theoretic analysis of the inflectional regular-irregular gradient for optimal processing units

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

Keywords

Morphological inflection, prediction-driven processing, discriminability, non-linearity, learnability

CNR authors

Pirrelli Vito, Marzi Claudia

CNR institutes

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

External links

OAI-PMH: Dublin Core

OAI-PMH: Mods

OAI-PMH: RDF

URL: http://www.nytud.hu/imm20/abstracts/main.pdf

External IDs

CNR OAI-PMH: oai:it.cnr:prodotti:471259