@inproceedings{kuznetsov-gurevych-2018-corpus,
title = "Corpus-Driven Thematic Hierarchy Induction",
author = "Kuznetsov, Ilia and
Gurevych, Iryna",
editor = "Korhonen, Anna and
Titov, Ivan",
booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K18-1006",
doi = "10.18653/v1/K18-1006",
pages = "54--64",
abstract = "Thematic role hierarchy is a widely used linguistic tool to describe interactions between semantic roles and their syntactic realizations. Despite decades of dedicated research and numerous thematic hierarchy suggestions in the literature, this concept has not been used in NLP so far due to incompatibility and limited scope of existing hierarchies. We introduce an empirical framework for thematic hierarchy induction and evaluate several role ranking strategies on English and German full-text corpus data. We hypothesize that global thematic hierarchy induction is feasible, that a hierarchy can be induced from just fractions of training data and that resulting hierarchies apply cross-lingually. We evaluate these assumptions empirically.",
}
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%0 Conference Proceedings
%T Corpus-Driven Thematic Hierarchy Induction
%A Kuznetsov, Ilia
%A Gurevych, Iryna
%Y Korhonen, Anna
%Y Titov, Ivan
%S Proceedings of the 22nd Conference on Computational Natural Language Learning
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F kuznetsov-gurevych-2018-corpus
%X Thematic role hierarchy is a widely used linguistic tool to describe interactions between semantic roles and their syntactic realizations. Despite decades of dedicated research and numerous thematic hierarchy suggestions in the literature, this concept has not been used in NLP so far due to incompatibility and limited scope of existing hierarchies. We introduce an empirical framework for thematic hierarchy induction and evaluate several role ranking strategies on English and German full-text corpus data. We hypothesize that global thematic hierarchy induction is feasible, that a hierarchy can be induced from just fractions of training data and that resulting hierarchies apply cross-lingually. We evaluate these assumptions empirically.
%R 10.18653/v1/K18-1006
%U https://aclanthology.org/K18-1006
%U https://doi.org/10.18653/v1/K18-1006
%P 54-64
Markdown (Informal)
[Corpus-Driven Thematic Hierarchy Induction](https://aclanthology.org/K18-1006) (Kuznetsov & Gurevych, CoNLL 2018)
ACL
- Ilia Kuznetsov and Iryna Gurevych. 2018. Corpus-Driven Thematic Hierarchy Induction. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 54–64, Brussels, Belgium. Association for Computational Linguistics.