Abstract
As time passes words can acquire meanings they did not previously have, such as the ‘twitter post’ usage of ‘tweet’. We address how this can be detected from time-stamped raw text. We propose a generative model with senses dependent on times and context words dependent on senses but otherwise eternal, and a Gibbs sampler for estimation. We obtain promising parameter estimates for positive (resp. negative) cases of known sense emergence (resp non-emergence) and adapt the ‘pseudo-word’ technique (Schutze, 1992) to give a novel further evaluation via ‘pseudo-neologisms’. The question of ground-truth is also addressed and a technique proposed to locate an emergence date for evaluation purposes.- Anthology ID:
- C16-1129
- 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:
- 1362–1373
- Language:
- URL:
- https://aclanthology.org/C16-1129
- DOI:
- Cite (ACL):
- Martin Emms and Arun Kumar Jayapal. 2016. Dynamic Generative model for Diachronic Sense Emergence Detection. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1362–1373, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Dynamic Generative model for Diachronic Sense Emergence Detection (Emms & Jayapal, COLING 2016)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/C16-1129.pdf