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
Venues:
EMNLP | 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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2020.nlposs-1.19.pdf
Video:
 https://slideslive.com/38939757
Code
 d2klab/tomodapi