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README.md
Hands-On Natural Language Processing with Python
This is the code repository for Hands-On Natural Language Processing with Python, published by Packt.
A practical guide to applying deep learning architectures to your NLP applications
Natural language processing (NLP) has found its applications in various domains like web search, advertisements, customer service and with Deep Learning, we can bring high performance in these application areas. This book teaches you to leverage deep learning models in performing various NLP tasks; it also showcases the best practices in dealing with the NLP challenges.
This book covers the following exciting features:
- Implement semantic embedding of words to classify and find entities
- Convert word to vectors by training to implement arithmetic on words
- Train a deep learning model to detect classification of tweets, news
- Implement a question-answering model with search and RNN models
- Train models for various text classification datasets using CNN
If you feel this book is for you, get your copy today!
Instructions and Navigations
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
Following is what you need for this book: This book is primarily targeted towards data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Readers are expected to have basic proficiency in machine learning and Python.
With the following software and hardware list you can run all code files present in the book (Chapter 1-13).
Software and Hardware List
| Chapter | Software required | Hardware required |
|---|---|---|
| 2 | Anaconda(Python3 Version) | Download for any OSWindows |
| 4 | Anaconda(Python3 Version) | Download for any OSWindows |
| 5 | Anaconda(Python3 Version) | Download for any OSWindows |
| 6 | Anaconda(Python3 Version) | Download for any OSWindows |
| 8 | Anaconda(Python3 Version) | Download for any OSWindows |
| 9 | Anaconda(Python3 Version) | Download for any OSWindows |
| 10 | Anaconda(Python3 Version) | Download for any OSWindows |
| 11 | Anaconda(Python3 Version) | Download for any OSWindows |
- Natural Language Processing with TensorFlow [Packt][Amazon]
- Mastering Natural Language Processing with Python [Packt][Amazon]
Rajesh Arumugam is an ML developer at SAP, Singapore. Previously, he developed ML solutions for smart city development in areas such as passenger flow analysis in public transit systems and optimization of energy consumption in buildings when working with Centre for Social Innovation at Hitachi Asia, Singapore. He has published papers in conferences and has pending patents in storage and ML. He holds a PhD in computer engineering from Nanyang Technological University, Singapore.
Rajalingappaa Shanmugamani is a deep learning lead at SAP, Singapore. Previously, he worked and consulted at various start-ups for developing computer vision products. He has a masters from IIT Madras, where his thesis was based on applications of computer vision in manufacturing. He has published articles in peer-reviewed journals and conferences and applied for a few patents in ML. In his spare time, he teaches programming and machine learning to school students and engineers.
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About
Hands On Natural Language Processing with Python, published by Packt
Natural Language Processing with Python Steven Bird 2009
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Current text-based question answering (QA) systems usually contain a named entity recogniser (NER) as a core component. Named entity recognition has traditionally been developed as a component for information extraction systems, and current techniques are focused on this end use. However, no formal assessment has been done on the characteristics of a NER within the task of question answering. In this paper we present a NER that aims at higher recall by allowing multiple entity labels to strings. The NER is .
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Abstract NLTK, the Natural Language Toolkit, is an open source project whose goals include providing students with software and language resources that will help them to learn basic NLP. Until now, the program modules in NLTK have covered such topics as tagging, chunking, and parsing, but have not incorporated any aspect of semantic interpretation. This paper describes recent work on building a new semantics package for NLTK.
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Python Natural Language Processing, published by Packt
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PacktPublishing/Python-Natural-Language-Processing
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README.md
Python Natural Language Processing
This is the code repository for Python Natural Language Processing, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.
This book starts off by laying the foundation for Natural Language Processing and gives you a better understanding of available free forms of corpus and different types of dataset. After this, you will know how to choose a dataset for natural language processing applications and find the right NLP techniques to process sentences in datasets and understand their structure. You will also learn how to tokenize different parts of sentences and ways to analyze them.
Instructions and Navigation
All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.
The code will look like the following:
import nltk from nltk.corpus import brown as cb from nltk.corpus import gutenberg as cg Let’s discuss some prerequisites for this book. Don’t worry, it’s not math or statistics, just basic Python coding syntax is all you need to know. Apart from that, you need Python 2.7.X or Python 3.5.X installed on your computer; I recommend using any Linux operating system as well. The list of Python dependencies can be found at GitHub repository at https://github.com/jalajthanaki/NLPython/blob/master/pip-requirements.txt. Now let’s look at the hardware required for this book. A computer with 4 GB RAM and at least a two-core CPU is good enough to execute the code, but for machine learning and deep learning examples, you may have more RAM, perhaps 8 GB or 16 GB, and computational power that uses GPU(s).
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.
About
Python Natural Language Processing, published by Packt