Time-Independent and Language-Independent Extraction of Multiword Expressions From Twitter

Nikhil Londhe, Rohini Srihari, Vishrawas Gopalakrishnan


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
Editors:
Yuji Matsumoto, Rashmi Prasad
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
2269–2278
Language:
URL:
https://aclanthology.org/C16-1214
DOI:
Bibkey:
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)
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PDF:
https://aclanthology.org/C16-1214.pdf