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        <title>Apples to Apples: A Systematic Evaluation of Topic Models</title>
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    <abstract>From statistical to neural models, a wide variety of topic modelling algorithms have been proposed in the literature. However, because of the diversity of datasets and metrics, there have not been many efforts to systematically compare their performance on the same benchmarks and under the same conditions. In this paper, we present a selection of 9 topic modelling techniques from the state of the art reflecting a diversity of approaches to the task, an overview of the different metrics used to compare their performance, and the challenges of conducting such a comparison. We empirically evaluate the performance of these models on different settings reflecting a variety of real-life conditions in terms of dataset size, number of topics, and distribution of topics, following identical preprocessing and evaluation processes. Using both metrics that rely on the intrinsic characteristics of the dataset (different coherence metrics), as well as external knowledge (word embeddings and ground-truth topic labels), our experiments reveal several shortcomings regarding the common practices in topic models evaluation.</abstract>
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%0 Conference Proceedings
%T Apples to Apples: A Systematic Evaluation of Topic Models
%A Harrando, Ismail
%A Lisena, Pasquale
%A Troncy, Raphael
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F harrando-etal-2021-apples
%X From statistical to neural models, a wide variety of topic modelling algorithms have been proposed in the literature. However, because of the diversity of datasets and metrics, there have not been many efforts to systematically compare their performance on the same benchmarks and under the same conditions. In this paper, we present a selection of 9 topic modelling techniques from the state of the art reflecting a diversity of approaches to the task, an overview of the different metrics used to compare their performance, and the challenges of conducting such a comparison. We empirically evaluate the performance of these models on different settings reflecting a variety of real-life conditions in terms of dataset size, number of topics, and distribution of topics, following identical preprocessing and evaluation processes. Using both metrics that rely on the intrinsic characteristics of the dataset (different coherence metrics), as well as external knowledge (word embeddings and ground-truth topic labels), our experiments reveal several shortcomings regarding the common practices in topic models evaluation.
%U https://aclanthology.org/2021.ranlp-1.55
%P 483-493

Markdown (Informal)

[Apples to Apples: A Systematic Evaluation of Topic Models](https://aclanthology.org/2021.ranlp-1.55) (Harrando et al., RANLP 2021)

• Apples to Apples: A Systematic Evaluation of Topic Models (Harrando et al., RANLP 2021)

ACL

• Ismail Harrando, Pasquale Lisena, and Raphael Troncy. 2021. Apples to Apples: A Systematic Evaluation of Topic Models. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 483–493, Held Online. INCOMA Ltd..