Corpus-based Identification of Verbs Participating in Verb Alternations Using Classification and Manual Annotation

Esther Seyffarth, Laura Kallmeyer

Abstract
English verb alternations allow participating verbs to appear in a set of syntactically different constructions whose associated semantic frames are systematically related. We use ENCOW and VerbNet data to train classifiers to predict the instrument subject alternation and the causative-inchoative alternation, relying on count-based and vector-based features as well as perplexity-based language model features, which are intended to reflect each alternation’s felicity by simulating it. Beyond the prediction task, we use the classifier results as a source for a manual annotation step in order to identify new, unseen instances of each alternation. This is possible because existing alternation datasets contain positive, but no negative instances and are not comprehensive. Over several sequences of classification-annotation steps, we iteratively extend our sets of alternating verbs. Our hybrid approach to the identification of new alternating verbs reduces the required annotation effort by only presenting annotators with the highest-scoring candidates from the previous classification. Due to the success of semi-supervised and unsupervised features, our approach can easily be transferred to further alternations.
Anthology ID:
2020.coling-main.357
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
4044–4055
Language:
URL:
https://aclanthology.org/2020.coling-main.357
DOI:
10.18653/v1/2020.coling-main.357
Bibkey:
Cite (ACL):
Esther Seyffarth and Laura Kallmeyer. 2020. Corpus-based Identification of Verbs Participating in Verb Alternations Using Classification and Manual Annotation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4044–4055, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Corpus-based Identification of Verbs Participating in Verb Alternations Using Classification and Manual Annotation (Seyffarth & Kallmeyer, COLING 2020)
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