Natural Language Processing and Computational Linguistics

A practical guide to text analysis with Python, Gensim, spaCy, and Keras

Bhargav Srinivasa-Desikan 지음

Packt(GCO Science) 2018년 06월 29일 출간

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▶TABLE of CONTENTS
1: WHAT IS TEXT ANALYSIS?
2: PYTHON TIPS FOR TEXT ANALYSIS
3: SPACY'S LANGUAGE MODELS
4: GENSIM ？ VECTORIZING TEXT AND TRANSFORMATIONS AND N-GRAMS
5: POS-TAGGING AND ITS APPLICATIONS
6: NER-TAGGING AND ITS APPLICATIONS
7: DEPENDENCY PARSING
8: TOPIC MODELS
9: ADVANCED TOPIC MODELING
10: CLUSTERING AND CLASSIFYING TEXT
11: SIMILARITY QUERIES AND SUMMARIZATION
12: WORD2VEC, DOC2VEC, AND GENSIM
13: DEEP LEARNING FOR TEXT
14: KERAS AND SPACY FOR DEEP LEARNING
15: SENTIMENT ANALYSIS AND CHATBOTS

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▶What this book covers
？ Chapter 1, What is Text Analysis? There is no time like now to do text analysis - we have an abundance of easily available data, powerful and free open source tools to conduct our analysis and research on Machine Learning, Computational Linguistics, and computing with text is progressing at a pace we have not seen before. In this chapter, we will go into details about what exactly text analysis is, and the motivations for studying and understanding text analysis.

？ Chapter 2, Python Tips for Text Analysis. We mentioned in Chapter 1, What is Text Analysis, that we will be using Python throughout the book because it is an easy-to-use and powerful language. In this chapter, we will substantiate these claims, while also providing a revision course in basic Python for text analysis. Why is this important? While we expect readers of the book to have a background in Python and high-school math, it is still possible that it’s been a while since you’ve written Python code - and even if you have, Python code you write during text analysis and string manipulation is quite different from, say, building a website using the web framework Django.

？ Chapter 3, spaCy’s Language Models. While we introduced text analysis in the previous chapter, we did not discuss any of the technical details behind building a text analysis pipeline. In this chapter, we will introduce you to spaCy’s Language Model - these will serve as the first step in text analysis, and are the first building block in our pipelines. Also, we will introduce the reader to spaCy and how we can use spaCy to help us in our text analysis tasks, as well as talk about some of it’s more powerful functionalities, such as POStagging and NER-tagging. We will finish up with an example of how we can preprocess data quickly and efficiently using spaCy.

？ Chapter 4, Gensim ？Vectorizing Text and Transformations and n-grams. While we have worked with raw textual data so far, any Machine Learning or information retrieval related algorithm will not accept data like this - which is why we use mathematical constructs called Vectors to help let the algorithms make sense of the text. We will introduce gensim as the tool to conduct this transformation, as well as scikit-learn, which will be used before we plug in the text to any sort of further analysis. A huge part of preprocessing is carried on over when we start our vectorization - bi-grams, tri-grams, and n-grams, as well using term frequencies to get rid of some words which we deem to not be useful.

？ Chapter 5, POS-Tagging and Its Applications. Chapters 1 and 2 introduced text analysis and Python, and chapters 3 and 4 helped us set-up our code for more advanced text analysis. This chapter discusses the first of such advanced techniques - part of speech tagging, popularly called POS-tagging. We will study what parts of speech exist, how to identify them in our documents, and what possible uses these POS-tags have.

？ Chapter 6, NER-Tagging and Its Applications. In the previous chapter, we saw how we can use spaCy’s language pipeline - POS-tagging is a very powerful tool, and we will now explore itsanother interesting usage, NER-tagging. We will discuss what exactly this is from a both linguistic and text analysis point of view, as well as detailing examples of its usage, and how to train our own NER-tagger with spaCy.

？ Chapter 7, Dependency Parsing. We saw in Chapters 5 and 6 how spaCy’s language pipeline performs a variety of complex Computational Linguistics algorithms, such as POS-tagging and NER-tagging. This isn’t all spaCy packs though, and in this chapter we will explore the power of dependency parsing and how it can be used in a variety of contexts and applications. We will have a look at the theory of dependency parsing before moving on to using it with spaCy, as well as training our own dependency parsers.

？ Chapter 8, Topic Models. Until now, we dealt with ...

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▶ Preface
Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data.

This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy.

You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, using realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning.

This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis.

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저자(글) 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 has a deep interest in modern text analysis. 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.