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A Glimpse Under the Surface: Language Understanding May Need Deep Syntactic Structure Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Adding Thesaurus Information into Probabilistic Topic Models

Authors : Natalia Loukachevitch, Michael Nokel

Published in: Text, Speech, and Dialogue

Publisher: Springer International Publishing

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Abstract

In this paper we present an approach of introducing thesaurus information into probabilistic topic models. The main idea of the approach is based on the assumption that the frequencies of semantically related words and phrases, which met in the same texts, should be enhanced and this action leads to their larger contribution into topics found in these texts. The experiments demonstrate that the direct implementation of this idea using WordNet synonyms or direct relations leads to great degradation of the initial model. But the correction of the initial assumption improves the model and makes it better than the initial model in several measures. Adding ngrams in similar manner further improves the model.

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Footnotes
1
\(n_{dw}\) is the frequency of w in the document d.
 
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Metadata
Title
Adding Thesaurus Information into Probabilistic Topic Models
Authors
Natalia Loukachevitch
Michael Nokel
Copyright Year
2017
Book
Text, Speech, and Dialogue

Print ISBN: 978-3-319-64205-5

Electronic ISBN: 978-3-319-64206-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-64206-2_24

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