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Natural Language Processing and Computational Linguistics

You're reading from  Natural Language Processing and Computational Linguistics

Product type Book
Published in Jun 2018
Publisher Packt
ISBN-13 9781788838535
Pages 306 pages
Edition 1st Edition
Languages
Author (1):
Bhargav Srinivasa-Desikan Bhargav Srinivasa-Desikan
Profile icon Bhargav Srinivasa-Desikan

Table of Contents (22) Chapters

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
What is Text Analysis? Python Tips for Text Analysis spaCy's Language Models Gensim – Vectorizing Text and Transformations and n-grams POS-Tagging and Its Applications NER-Tagging and Its Applications Dependency Parsing Topic Models Advanced Topic Modeling Clustering and Classifying Text Similarity Queries and Summarization Word2Vec, Doc2Vec, and Gensim Deep Learning for Text Keras and spaCy for Deep Learning Sentiment Analysis and ChatBots Other Books You May Enjoy Index

Vectors and why we need them


We're now moving toward the machine learning part of text analysis - this means that we will now start playing a little less with words and a little more with numbers. Even when we used spaCy, the POS-tagging and NER-tagging, for example, was done through statistical models - but the inner workings were largely hidden for us - we passed over Unicode text and after some magic, we have annotated text.

For Gensim however, we're expected to pass vectors as inputs to the IR algorithms (such as LDA or LSI), largely because what's going on under the hood is mathematical operations involving matrices. This means that we have to represent what was previously a string as a vector - and these kind of representations or models are called Vector Space Models [9].

From a mathematical perspective, a vector is a geometric object that has magnitude and direction. We don't need to pay as much attention to this, and rather think of vectors as a way of projecting words onto a mathematical...

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