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Abstract:

This paper presents a study on the automatic classification of default and non- default codings for aspect-marked verbs in six Slavic and one Baltic language. As clas- sifier a Support Vector Machine (SVM) and as verbal features Shannon Information (SI) and Average Information Content (IC) have been utilised. In all languages high accuracy of the classification has been achieved. In addition, we found indications for the validity of the Uniform Information Density principle within SI and IC.

Type: Proceedings

Title: Predicting default and non-default aspectual coding: Impact and density of information features
Year: 2019
Organization:Proceedings of the 3rd Workshop on Natural Language for Artificial Intelligence co-located with the 18th International Conference of the Italian Association for Artificial Intelligence (AIIA 2019)
Address:
@PROCEEDINGS{RiYou2,
TITLE = {Predicting default and non-default aspectual coding: Impact and density of information features},
YEAR = {2019},
ORGANIZATION = {Proceedings of the 3rd Workshop on Natural Language for Artificial Intelligence co-located with the 18th International Conference of the Italian Association for Artificial Intelligence (AIIA 2019)}
}