Estimation of Average Information Content: Comparison of Impact of Contexts
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10427043" target="_blank" >RIV/00216208:11320/19:10427043 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
Estimation of Average Information Content: Comparison of Impact of Contexts
Original language description
In this paper, we compare Linear Mixed Effect Models (LMM) which utilise the predictors Average Information Content (IC) and frequency for the prediction of lengths of aspect-marked verbs. IC is the information which target elements convey to their context. Focusing on typologically diverse languages, we took as contexts dependency frames and n-grams, and found that IC estimated from n-grams outperforms IC estimated from dependency frames: the models which utilise IC from n-grams achieve high correlations between predicted and actual verbs’ lengths, while models which utilise IC form dependency frames perform poorly. Only in few languages we found prediction effects of IC.
Czech name
—
Czech description
—
Classification
Type
O - Miscellaneous
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
—
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů