Word Similarity Datasets for Indian Languages: Annotation and Baseline Systems
Syed Sarfaraz Akhtar, Arihant Gupta, Avijit Vajpayee, Arjit Srivastava, Manish Shrivastava
Abstract
With the advent of word representations, word similarity tasks are becoming increasing popular as an evaluation metric for the quality of the representations. In this paper, we present manually annotated monolingual word similarity datasets of six Indian languages - Urdu, Telugu, Marathi, Punjabi, Tamil and Gujarati. These languages are most spoken Indian languages worldwide after Hindi and Bengali. For the construction of these datasets, our approach relies on translation and re-annotation of word similarity datasets of English. We also present baseline scores for word representation models using state-of-the-art techniques for Urdu, Telugu and Marathi by evaluating them on newly created word similarity datasets.- Anthology ID:
- W17-0811
- Volume:
- Proceedings of the 11th Linguistic Annotation Workshop
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Nathan Schneider, Nianwen Xue
- Venue:
- LAW
- SIG:
- SIGANN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 91–94
- Language:
- URL:
- https://aclanthology.org/W17-0811
- DOI:
- 10.18653/v1/W17-0811
- Cite (ACL):
- Syed Sarfaraz Akhtar, Arihant Gupta, Avijit Vajpayee, Arjit Srivastava, and Manish Shrivastava. 2017. Word Similarity Datasets for Indian Languages: Annotation and Baseline Systems. In Proceedings of the 11th Linguistic Annotation Workshop, pages 91–94, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Word Similarity Datasets for Indian Languages: Annotation and Baseline Systems (Akhtar et al., LAW 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W17-0811.pdf