Abstract
Parsing texts into universal dependencies (UD) in realistic scenarios requires infrastructure for the morphological analysis and disambiguation (MA&D) of typologically different languages as a first tier. MA&D is particularly challenging in morphologically rich languages (MRLs), where the ambiguous space-delimited tokens ought to be disambiguated with respect to their constituent morphemes, each morpheme carrying its own tag and a rich set features. Here we present a novel, language-agnostic, framework for MA&D, based on a transition system with two variants — word-based and morpheme-based — and a dedicated transition to mitigate the biases of variable-length morpheme sequences. Our experiments on a Modern Hebrew case study show state of the art results, and we show that the morpheme-based MD consistently outperforms our word-based variant. We further illustrate the utility and multilingual coverage of our framework by morphologically analyzing and disambiguating the large set of languages in the UD treebanks.- Anthology ID:
- C16-1033
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 337–348
- Language:
- URL:
- https://aclanthology.org/C16-1033
- DOI:
- Cite (ACL):
- Amir More and Reut Tsarfaty. 2016. Data-Driven Morphological Analysis and Disambiguation for Morphologically Rich Languages and Universal Dependencies. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 337–348, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Data-Driven Morphological Analysis and Disambiguation for Morphologically Rich Languages and Universal Dependencies (More & Tsarfaty, COLING 2016)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/C16-1033.pdf
- Code
- habeanf/yap