Abstract
Ambiguity represents an obstacle for distributional semantic models(DSMs), which typically subsume the contexts of all word senses within one vector. While individual vector space approaches have been concerned with sense discrimination (e.g., Schütze 1998, Erk 2009, Erk and Pado 2010), such discrimination has rarely been integrated into DSMs across semantic tasks. This paper presents a soft-clustering approach to sense discrimination that filters sense-irrelevant features when predicting the degrees of compositionality for German noun-noun compounds and German particle verbs.- Anthology ID:
- W17-1708
- Volume:
- Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Stella Markantonatou, Carlos Ramisch, Agata Savary, Veronika Vincze
- Venue:
- MWE
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 66–72
- Language:
- URL:
- https://aclanthology.org/W17-1708
- DOI:
- 10.18653/v1/W17-1708
- Cite (ACL):
- Stefan Bott and Sabine Schulte im Walde. 2017. Factoring Ambiguity out of the Prediction of Compositionality for German Multi-Word Expressions. In Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017), pages 66–72, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Factoring Ambiguity out of the Prediction of Compositionality for German Multi-Word Expressions (Bott & Schulte im Walde, MWE 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W17-1708.pdf