Predicting default and non-default aspectual coding: Impact and density of information features

Michael Richter; Tariq Yousef

Predicting default and non-default aspectual coding: Impact and density of information features

Leipzig University

Research output: Contribution to journal › Conference article › Research › peer-review

Abstract

This paper presents a study on the automatic classification of default and nondefault codings for aspect-marked verbs in six Slavic and one Baltic language. As classifier 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.

Original language	English
Journal	CEUR Workshop Proceedings
Volume	2521
ISSN	1613-0073
Publication status	Published - 2019
Event	3rd Workshop on Natural Language for Artificial Intelligence, NL4AI 2019 - Rende, Italy Duration: 19. Nov 2019 → 22. Nov 2019

Conference

Conference	3rd Workshop on Natural Language for Artificial Intelligence, NL4AI 2019
Country/Territory	Italy
City	Rende
Period	19/11/2019 → 22/11/2019

Bibliographical note

Funding Information:
* Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundatino) project number: 357550571.

Publisher Copyright:
Copyright © 2019 for this paper by its authors.

Keywords

Coding
Information content
Verb aspect

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title = "Predicting default and non-default aspectual coding: Impact and density of information features",

abstract = "This paper presents a study on the automatic classification of default and nondefault codings for aspect-marked verbs in six Slavic and one Baltic language. As classifier 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.",

keywords = "Coding, Information content, Verb aspect",

author = "Michael Richter and Tariq Yousef",

note = "Funding Information: * Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundatino) project number: 357550571. Publisher Copyright: Copyright {\textcopyright} 2019 for this paper by its authors.; 3rd Workshop on Natural Language for Artificial Intelligence, NL4AI 2019 ; Conference date: 19-11-2019 Through 22-11-2019",

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AU - Richter, Michael

AU - Yousef, Tariq

N1 - Funding Information: * Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundatino) project number: 357550571. Publisher Copyright: Copyright © 2019 for this paper by its authors.

PY - 2019

Y1 - 2019

N2 - This paper presents a study on the automatic classification of default and nondefault codings for aspect-marked verbs in six Slavic and one Baltic language. As classifier 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.

AB - This paper presents a study on the automatic classification of default and nondefault codings for aspect-marked verbs in six Slavic and one Baltic language. As classifier 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.

KW - Coding

KW - Information content

KW - Verb aspect

UR - http://www.scopus.com/inward/record.url?scp=85077506667&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85077506667

SN - 1613-0073

VL - 2521

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

Y2 - 19 November 2019 through 22 November 2019

ER -

Predicting default and non-default aspectual coding: Impact and density of information features

Abstract

Conference

Bibliographical note

Keywords

SDU Link

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