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You're reading from  Natural Language Processing and Computational Linguistics

Product typeBook
Published inJun 2018
Reading LevelBeginner
PublisherPackt
ISBN-139781788838535
Edition1st Edition
Languages
Python
Tools
Spacy
Concepts
Mobile Application Development
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Author (1)
Bhargav Srinivasa-Desikan
Bhargav Srinivasa-Desikan
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Bhargav Srinivasa-Desikan

Bhargav Srinivasa-Desikan is a research engineer working for INRIA in Lille, France. He is a part of the MODAL (Models of Data Analysis and Learning) team, and he works on metric learning, predictor aggregation, and data visualization. He is a regular contributor to the Python open source community, and completed Google Summer of Code in 2016 with Gensim where he implemented Dynamic Topic Models. He is a regular speaker at PyCons and PyDatas across Europe and Asia, and conducts tutorials on text analysis using Python.
Read more about Bhargav Srinivasa-Desikan

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Summary

In this chapter, we saw our first usage of Gensim's machine learning algorithms, and in particular, topic models. Topic models are a great way for us to work with unlabeled data, and they help us find underlying structures in text. There are multiple ways for us to identify topics in the text, with LDA, LSI, HDP, and NNMF being the most popular methods, and we have discussed ways to use all these methods in both scikit-learn and Gensim.

In the next chapter, we will move into advanced operations using topic models.

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Natural Language Processing and Computational Linguistics
Published in: Jun 2018Publisher: PacktISBN-13: 9781788838535
© 2018 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Bhargav Srinivasa-Desikan

Bhargav Srinivasa-Desikan is a research engineer working for INRIA in Lille, France. He is a part of the MODAL (Models of Data Analysis and Learning) team, and he works on metric learning, predictor aggregation, and data visualization. He is a regular contributor to the Python open source community, and completed Google Summer of Code in 2016 with Gensim where he implemented Dynamic Topic Models. He is a regular speaker at PyCons and PyDatas across Europe and Asia, and conducts tutorials on text analysis using Python.
Read more about Bhargav Srinivasa-Desikan

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