Monitoring geometrical properties of word embeddings for detecting the emergence of new topics.
Clément Christophe, Julien Velcin, Jairo Cugliari, Manel Boumghar, Philippe Suignard
Abstract
Slow emerging topic detection is a task between event detection, where we aggregate behaviors of different words on short period of time, and language evolution, where we monitor their long term evolution. In this work, we tackle the problem of early detection of slowly emerging new topics. To this end, we gather evidence of weak signals at the word level. We propose to monitor the behavior of words representation in an embedding space and use one of its geometrical properties to characterize the emergence of topics. As evaluation is typically hard for this kind of task, we present a framework for quantitative evaluation and show positive results that outperform state-of-the-art methods. Our method is evaluated on two public datasets of press and scientific articles.- Anthology ID:
- 2021.emnlp-main.76
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 994–1003
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.76
- DOI:
- 10.18653/v1/2021.emnlp-main.76
- Cite (ACL):
- Clément Christophe, Julien Velcin, Jairo Cugliari, Manel Boumghar, and Philippe Suignard. 2021. Monitoring geometrical properties of word embeddings for detecting the emergence of new topics.. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 994–1003, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Monitoring geometrical properties of word embeddings for detecting the emergence of new topics. (Christophe et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.emnlp-main.76.pdf