Sanskrit Sandhi Splitting using seq2(seq)2
Rahul Aralikatte, Neelamadhav Gantayat, Naveen Panwar, Anush Sankaran, Senthil Mani
Abstract
In Sanskrit, small words (morphemes) are combined to form compound words through a process known as Sandhi. Sandhi splitting is the process of splitting a given compound word into its constituent morphemes. Although rules governing word splitting exists in the language, it is highly challenging to identify the location of the splits in a compound word. Though existing Sandhi splitting systems incorporate these pre-defined splitting rules, they have a low accuracy as the same compound word might be broken down in multiple ways to provide syntactically correct splits. In this research, we propose a novel deep learning architecture called Double Decoder RNN (DD-RNN), which (i) predicts the location of the split(s) with 95% accuracy, and (ii) predicts the constituent words (learning the Sandhi splitting rules) with 79.5% accuracy, outperforming the state-of-art by 20%. Additionally, we show the generalization capability of our deep learning model, by showing competitive results in the problem of Chinese word segmentation, as well.- Anthology ID:
- D18-1530
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4909–4914
- Language:
- URL:
- https://aclanthology.org/D18-1530
- DOI:
- 10.18653/v1/D18-1530
- Cite (ACL):
- Rahul Aralikatte, Neelamadhav Gantayat, Naveen Panwar, Anush Sankaran, and Senthil Mani. 2018. Sanskrit Sandhi Splitting using seq2(seq)2. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4909–4914, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Sanskrit Sandhi Splitting using seq2(seq)2 (Aralikatte et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/D18-1530.pdf