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2021 | OriginalPaper | Buchkapitel

Evaluating the Effect of Corpus Normalisation in Topics Coherence

verfasst von : Luana da Silva Sousa, Vinicius Melquiades de Sousa, Rogerio de Aquino Silva, Gustavo Medeiros de Araújo

Erschienen in: Data and Information in Online Environments

Verlag: Springer International Publishing

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Abstract

Probabilistic topic models are extensively used to better understand the content of documents. Due to the fact that topic models are totally unsupervised, statistical and data driven, they may produce topics not always meaningful. This work is based on the hypothesis that, since LDA takes into account the number of occurrences of words, we could affect the quality of topics by semantically normalising the text, where each concept would be represented by the same word. We can find a formal description of lexemes found in text using a knowledgebase and extract the several forms of mentioning a lexeme to normalize a corpus. We use topic coherence metric, as it represents the semantic interpretability of the terms used to describe a particular topic, to quantify the influence of semantic corpus normalisation in topics. The first tests on the semantic normalisation framework of texts showed prominent results, and shall be investigated in depth in future.

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Titel: Evaluating the Effect of Corpus Normalisation in Topics Coherence
verfasst von: Luana da Silva Sousa
Vinicius Melquiades de Sousa
Rogerio de Aquino Silva
Gustavo Medeiros de Araújo
Verlag: Springer International Publishing
Buch: Data and Information in Online Environments
Print ISBN: 978-3-030-77416-5

Electronic ISBN: 978-3-030-77417-2

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-77417-2_15

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