A flexible and easy-to-use semantic role labeling framework for different languages

Quynh Ngoc Thi Do, Artuur Leeuwenberg, Geert Heyman, Marie Francine Moens

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

Abstract

This paper presents DAMESRL, a flexible and open source framework for deep semantic role labeling (SRL). DAMESRL aims to facilitate easy exploration of model structures for multiple languages with different characteristics. It provides flexibility in its model construction in terms of word representation, sequence representation, output modeling, and inference styles and comes with clear output visualization. Additionally, it handles various input and output formats and comes with clear output visualization. The framework is available under the Apache 2.0 license.

Original languageEnglish
Title of host publicationCOLING 2018 - 27th International Conference on Computational Linguistics, Proceedings of System Demonstrations
PublisherAssociation for Computational Linguistics (ACL)
Pages161-165
Number of pages5
ISBN (Electronic)9781948087537
Publication statusPublished - 2018
Externally publishedYes
Event27th International Conference on Computational Linguistics, COLING 2018 - Santa Fe, United States
Duration: 20 Aug 201826 Aug 2018

Publication series

NameCOLING 2018 - 27th International Conference on Computational Linguistics, Proceedings of System Demonstrations

Conference

Conference27th International Conference on Computational Linguistics, COLING 2018
Country/TerritoryUnited States
CitySanta Fe
Period20/08/1826/08/18

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