Natural Language Processing list 3

Technical Challenges and Design Issues in Bangla Language Processing

by
Language: English
Release Date: April 30, 2013

Many take advantage of software and hardware accessibility in the English language. However, for non native speakers, this inevitably becomes a problem; specifically for the complex Bangla language which is not easily integrated into the world of technology. Technical Challenges and Design Issues in...

R Deep Learning Essentials

A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet, 2nd Edition

by Mark Hodnett, Joshua F. Wiley
Language: English
Release Date: August 24, 2018

Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNet Key Features Use R 3.5 for building deep learning models for computer vision and text Apply deep learning techniques in cloud for large-scale processing Build, train, and optimize neural network models on a range of datasets Book...

Advances in Chinese Document and Text Processing

by Cheng-Lin Liu, Yue Lu
Language: English
Release Date: March 14, 2017

The book is a collection of invited chapters by experts in Chinese document and text processing, and is part of a series on Language Processing, Pattern Recognition, and Intelligent Systems. The chapters introduce the latest advances and state-of-the-art methods for Chinese document image analysis...

PyTorch Deep Learning Hands-On

Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily

by Sherin Thomas, Sudhanshu Passi
Language: English
Release Date: April 30, 2019

Hands-on projects cover all the key deep learning methods built step-by-step in PyTorch Key Features Internals and principles of PyTorch Implement key deep learning methods in PyTorch: CNNs, GANs, RNNs, reinforcement learning, and more Build deep learning workflows and take deep learning models...

Web Corpus Construction

by Roland Schäfer, Felix Bildhauer
Language: English
Release Date: July 1, 2013

The World Wide Web constitutes the largest existing source of texts written in a great variety of languages. A feasible and sound way of exploiting this data for linguistic research is to compile a static corpus for a given language. There are several adavantages of this approach: (i) Working with such...

Hands-On Transfer Learning with Python

Implement advanced deep learning and neural network models using TensorFlow and Keras

by Dipanjan Sarkar, Raghav Bali, Tamoghna Ghosh
Language: English
Release Date: August 31, 2018

Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem Key Features Build deep learning models with transfer learning principles in Python implement transfer learning to solve real-world research problems Perform complex...

Automatic Text Simplification

by Horacio Saggion, Graeme Hirst
Language: English
Release Date: April 25, 2017

Thanks to the availability of texts on the Web in recent years, increased knowledge and information have been made available to broader audiences. However, the way in which a text is written—its vocabulary, its syntax—can be difficult to read and understand for many people, especially those with...

Text Mining with R

A Tidy Approach

by Julia Silge, David Robinson
Language: English
Release Date: June 12, 2017

Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed...

Web Corpus Construction

by Roland Schäfer, Felix Bildhauer
Language: English
Release Date: July 1, 2013

The World Wide Web constitutes the largest existing source of texts written in a great variety of languages. A feasible and sound way of exploiting this data for linguistic research is to compile a static corpus for a given language. There are several adavantages of this approach: (i) Working with...

Domain-Sensitive Temporal Tagging

by Jannik Strötgen, Michael Gertz
Language: English
Release Date: July 28, 2016

This book covers the topic of temporal tagging, the detection of temporal expressions and the normalization of their semantics to some standard format. It places a special focus on the challenges and opportunities of domain-sensitive temporal tagging. After providing background knowledge on the concept...

Semi-Supervised Dependency Parsing

by Wenliang Chen, Min Zhang
Language: English
Release Date: July 16, 2015

This book presents a comprehensive overview of semi-supervised approaches to dependency parsing. Having become increasingly popular in recent years, one of the main reasons for their success is that they can make use of large unlabeled data together with relatively small labeled data and have shown...

Markov Logic

An Interface Layer for Artificial Intelligence

by Pedro Domingos, Daniel Lowd, Ronald Brachman
Language: English
Release Date: May 8, 2009

Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. First-order logic...

IBM Watson Projects

Eight exciting projects that put artificial intelligence into practice for optimal business performance

by James D. Miller
Language: English
Release Date: September 29, 2018

Incorporate intelligence to your data-driven business insights and high accuracy business solutions Key Features Explore IBM Watson capabilities such as Natural Language Processing (NLP) and machine learning Build projects to adopt IBM Watson across retail, banking, and healthcare Learn forecasting,...

Machine Learning for Healthcare Analytics Projects

Build smart AI applications using neural network methodologies across the healthcare vertical market

by Eduonix Learning Solutions
Language: English
Release Date: October 30, 2018

Create real-world machine learning solutions using NumPy, pandas, matplotlib, and scikit-learn Key Features Develop a range of healthcare analytics projects using real-world datasets Implement key machine learning algorithms using a range of libraries from the Python ecosystem Accomplish intermediate-to-complex...