TOMODAPI: A Topic Modeling API to Train, Use and Compare Topic Models
Pasquale Lisena, Ismail Harrando, Oussama Kandakji, Raphael Troncy
Abstract
From LDA to neural models, different topic modeling approaches have been proposed in the literature. However, their suitability and performance is not easy to compare, particularly when the algorithms are being used in the wild on heterogeneous datasets. In this paper, we introduce ToModAPI (TOpic MOdeling API), a wrapper library to easily train, evaluate and infer using different topic modeling algorithms through a unified interface. The library is extensible and can be used in Python environments or through a Web API.- Anthology ID:
- 2020.nlposs-1.19
- Volume:
- Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Eunjeong L. Park, Masato Hagiwara, Dmitrijs Milajevs, Nelson F. Liu, Geeticka Chauhan, Liling Tan
- Venue:
- NLPOSS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 132–140
- Language:
- URL:
- https://aclanthology.org/2020.nlposs-1.19
- DOI:
- 10.18653/v1/2020.nlposs-1.19
- Cite (ACL):
- Pasquale Lisena, Ismail Harrando, Oussama Kandakji, and Raphael Troncy. 2020. TOMODAPI: A Topic Modeling API to Train, Use and Compare Topic Models. In Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS), pages 132–140, Online. Association for Computational Linguistics.
- Cite (Informal):
- TOMODAPI: A Topic Modeling API to Train, Use and Compare Topic Models (Lisena et al., NLPOSS 2020)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2020.nlposs-1.19.pdf
- Code
- d2klab/tomodapi