Abstract
We present an experimental study making use of a machine learning approach to identify the factors that affect the aspectual value that characterizes verbs under each of their readings. The study is based on various morpho-syntactic and semantic features collected from a French lexical resource and on a gold standard aspectual classification of verb readings designed by an expert. Our results support the tested hypothesis, namely that agentivity and abstractness influence lexical aspect.- Anthology ID:
- L16-1193
- Volume:
- Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
- Month:
- May
- Year:
- 2016
- Address:
- Portorož, Slovenia
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 1212–1220
- Language:
- URL:
- https://aclanthology.org/L16-1193
- DOI:
- Cite (ACL):
- Ingrid Falk and Fabienne Martin. 2016. Aspectual Flexibility Increases with Agentivity and ConcretenessA Computational Classification Experiment on Polysemous Verbs. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 1212–1220, Portorož, Slovenia. European Language Resources Association (ELRA).
- Cite (Informal):
- Aspectual Flexibility Increases with Agentivity and ConcretenessA Computational Classification Experiment on Polysemous Verbs (Falk & Martin, LREC 2016)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/L16-1193.pdf