Skip to Main content Skip to Navigation
Conference papers

TOMODAPI: A Topic Modeling API to Train, Use and Compare Topic Models

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.
Document type :
Conference papers
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03554622
Contributor : Centre De Documentation Eurecom Connect in order to contact the contributor
Submitted on : Thursday, February 3, 2022 - 12:06:15 PM
Last modification on : Friday, February 4, 2022 - 5:46:45 PM

Links full text

Identifiers

Collections

Citation

Pasquale Lisena, Ismail Harrando, Oussama Kandakji, Raphaël Troncy. TOMODAPI: A Topic Modeling API to Train, Use and Compare Topic Models. NLP-OSS 2020, Proceedings of Second Workshop for NLP Open Source Software, Nov 2020, Online, France. pp.132-140, ⟨10.18653/v1/2020.nlposs-1.19⟩. ⟨hal-03554622⟩

Share

Metrics

Record views

20