Abstract
Multiword Expressions (MWEs) are crucial lexico-semantic units in any language. However, most work on MWEs has been focused on standard monolingual corpora. In this work, we examine MWE usage on Twitter - an inherently multilingual medium with an extremely short average text length that is often replete with grammatical errors. In this work we present a new graph based, language agnostic method for automatically extracting MWEs from tweets. We show how our method outperforms standard Association Measures. We also present a novel unsupervised evaluation technique to ascertain the accuracy of MWE extraction.- Anthology ID:
- C16-1214
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 2269–2278
- Language:
- URL:
- https://aclanthology.org/C16-1214
- DOI:
- Cite (ACL):
- Nikhil Londhe, Rohini Srihari, and Vishrawas Gopalakrishnan. 2016. Time-Independent and Language-Independent Extraction of Multiword Expressions From Twitter. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2269–2278, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Time-Independent and Language-Independent Extraction of Multiword Expressions From Twitter (Londhe et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/C16-1214.pdf