Mastering Natural Language Processing with Python

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Packt Publishing Ltd, 2016 M06 10 - 238 páginas

Maximize your NLP capabilities while creating amazing NLP projects in Python

About This Book
  • Learn to implement various NLP tasks in Python
  • Gain insights into the current and budding research topics of NLP
  • This is a comprehensive step-by-step guide to help students and researchers create their own projects based on real-life applications
Who This Book Is For

This book is for intermediate level developers in NLP with a reasonable knowledge level and understanding of Python.

What You Will Learn
  • Implement string matching algorithms and normalization techniques
  • Implement statistical language modeling techniques
  • Get an insight into developing a stemmer, lemmatizer, morphological analyzer, and morphological generator
  • Develop a search engine and implement POS tagging concepts and statistical modeling concepts involving the n gram approach
  • Familiarize yourself with concepts such as the Treebank construct, CFG construction, the CYK Chart Parsing algorithm, and the Earley Chart Parsing algorithm
  • Develop an NER-based system and understand and apply the concepts of sentiment analysis
  • Understand and implement the concepts of Information Retrieval and text summarization
  • Develop a Discourse Analysis System and Anaphora Resolution based system
In Detail

Natural Language Processing is one of the fields of computational linguistics and artificial intelligence that is concerned with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning.

This book will give you expertise on how to employ various NLP tasks in Python, giving you an insight into the best practices when designing and building NLP-based applications using Python. It will help you become an expert in no time and assist you in creating your own NLP projects using NLTK.

You will sequentially be guided through applying machine learning tools to develop various models. We'll give you clarity on how to create training data and how to implement major NLP applications such as Named Entity Recognition, Question Answering System, Discourse Analysis, Transliteration, Word Sense disambiguation, Information Retrieval, Sentiment Analysis, Text Summarization, and Anaphora Resolution.

Style and approach

This is an easy-to-follow guide, full of hands-on examples of real-world tasks. Each topic is explained and placed in context, and for the more inquisitive, there are more details of the concepts used.

 

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Contenido

Working with Strings
1
Statistical Language Modeling
23
Morphology Getting Our Feet Wet
49
PartsofSpeech Tagging Identifying words
65
Parsing Analyzing Training Data
85
Semantic Analysis Meaning Matters
107
Sentiment Analysis I Am Happy
133
Information Retrieval Accessing Information
165
Discourse Analysis Knowing Is Believing
183
Evaluation of NLP Systems Analyzing Performance
199
Index
219
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Acerca del autor (2016)

Deepti Chopra is an Assistant Professor at Banasthali University. Her primary area of research is computational linguistics, Natural Language Processing, and artificial intelligence. She is also involved in the development of MT engines for English to Indian languages. She has several publications in various journals and conferences and also serves on the program committees of several conferences and journals.

Nisheeth Joshi works as an Associate Professor at Banasthali University. His areas of interest include computational linguistics, Natural Language Processing, and artificial intelligence. Besides this, he is also very actively involved in the development of MT engines for English to Indian languages. He is one of the experts empaneled with the TDIL program, Department of Information Technology, Govt. of India, a premier organization that oversees Language Technology Funding and Research in India. He has several publications in various journals and conferences and also serves on the program committees and editorial boards of several conferences and journals.

Iti Mathur is an Assistant Professor at Banasthali University. Her areas of interest are computational semantics and ontological engineering. Besides this, she is also involved in the development of MT engines for English to Indian languages. She is one of the experts empaneled with TDIL program, Department of Electronics and Information Technology (DeitY), Govt. of India, a premier organization that oversees Language Technology Funding and Research in India. She has several publications in various journals and conferences and also serves on the program committees and editorial boards of several conferences and journals.

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