@inproceedings{lisena-etal-2020-tomodapi,
title = "{TOMODAPI}: A Topic Modeling {API} to Train, Use and Compare Topic Models",
author = "Lisena, Pasquale and
Harrando, Ismail and
Kandakji, Oussama and
Troncy, Raphael",
editor = "Park, Eunjeong L. and
Hagiwara, Masato and
Milajevs, Dmitrijs and
Liu, Nelson F. and
Chauhan, Geeticka and
Tan, Liling",
booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlposs-1.19",
doi = "10.18653/v1/2020.nlposs-1.19",
pages = "132--140",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T TOMODAPI: A Topic Modeling API to Train, Use and Compare Topic Models
%A Lisena, Pasquale
%A Harrando, Ismail
%A Kandakji, Oussama
%A Troncy, Raphael
%Y Park, Eunjeong L.
%Y Hagiwara, Masato
%Y Milajevs, Dmitrijs
%Y Liu, Nelson F.
%Y Chauhan, Geeticka
%Y Tan, Liling
%S Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F lisena-etal-2020-tomodapi
%X 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.
%R 10.18653/v1/2020.nlposs-1.19
%U https://aclanthology.org/2020.nlposs-1.19
%U https://doi.org/10.18653/v1/2020.nlposs-1.19
%P 132-140
Markdown (Informal)
[TOMODAPI: A Topic Modeling API to Train, Use and Compare Topic Models](https://aclanthology.org/2020.nlposs-1.19) (Lisena et al., NLPOSS 2020)
ACL