Abstract
In the work presented here we assess the degree of compositionality of German Particle Verbs with a Distributional Semantics Model which only relies on word window information and has no access to syntactic information as such. Our method only takes the lexical distributional distance between the Particle Verb to its Base Verb as a predictor for compositionality. We show that the ranking of distributional similarity correlates significantly with the ranking of human judgements on semantic compositionality for a series of Particle Verbs and the Base Verbs they are derived from. We also investigate the influence of further linguistic factors, such as the ambiguity and the overall frequency of the verbs and a syntactically separate occurrences of verbs and particles that causes difficulties for the correct lemmatization of Particle Verbs. We analyse in how far these factors may influence the success with which the compositionality of the Particle Verbs may be predicted.- Anthology ID:
- L14-1704
- Volume:
- Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
- Month:
- May
- Year:
- 2014
- Address:
- Reykjavik, Iceland
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/921_Paper.pdf
- DOI:
- Cite (ACL):
- Stefan Bott and Sabine Schulte im Walde. 2014. Optimizing a Distributional Semantic Model for the Prediction of German Particle Verb Compositionality. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), Reykjavik, Iceland. European Language Resources Association (ELRA).
- Cite (Informal):
- Optimizing a Distributional Semantic Model for the Prediction of German Particle Verb Compositionality (Bott & Schulte im Walde, LREC 2014)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/921_Paper.pdf