Abstract
We suggest a new language-independent architecture of robust word vectors (RoVe). It is designed to alleviate the issue of typos, which are common in almost any user-generated content, and hinder automatic text processing. Our model is morphologically motivated, which allows it to deal with unseen word forms in morphologically rich languages. We present the results on a number of Natural Language Processing (NLP) tasks and languages for the variety of related architectures and show that proposed architecture is typo-proof.- Anthology ID:
- W18-6108
- Volume:
- Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
- Month:
- November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
- Venue:
- WNUT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 54–63
- Language:
- URL:
- https://aclanthology.org/W18-6108
- DOI:
- 10.18653/v1/W18-6108
- Cite (ACL):
- Valentin Malykh, Varvara Logacheva, and Taras Khakhulin. 2018. Robust Word Vectors: Context-Informed Embeddings for Noisy Texts. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, pages 54–63, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Robust Word Vectors: Context-Informed Embeddings for Noisy Texts (Malykh et al., WNUT 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W18-6108.pdf