Free Natural Language Annotation For Machine Learning Ebooks Online
Create your own natural language training corpus for machine learning. Whether you’re working with English, Chinese, or any other natural language, this hands-on book guides you through a proven annotation development cycle—the process of adding metadata to your training corpus to help ML algorithms work more efficiently. You don’t need any programming or linguistics experience to get started.Using detailed examples at every step, you’ll learn how the MATTER Annotation Development Process helps you Model, Annotate, Train, Test, Evaluate, and Revise your training corpus. You also get a complete walkthrough of a real-world annotation project.Define a clear annotation goal before collecting your dataset (corpus)Learn tools for analyzing the linguistic content of your corpusBuild a model and specification for your annotation projectExamine the different annotation formats, from basic XML to the Linguistic Annotation FrameworkCreate a gold standard corpus that can be used to train and test ML algorithmsSelect the ML algorithms that will process your annotated dataEvaluate the test results and revise your annotation taskLearn how to use lightweight software for annotating texts and adjudicating the annotationsThis book is a perfect companion to O’Reilly’s Natural Language Processing with Python.
Paperback: 342 pages
Publisher: O'Reilly Media; 1 edition (November 4, 2012)
Language: English
ISBN-10: 1449306667
ISBN-13: 978-1449306663
Product Dimensions: 7 x 0.7 x 9.2 inches
Shipping Weight: 1.5 pounds (View shipping rates and policies)
Average Customer Review: 4.5 out of 5 stars See all reviews (4 customer reviews)
Best Sellers Rank: #609,823 in Books (See Top 100 in Books) #54 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Natural Language Processing #306 in Books > Computers & Technology > Databases & Big Data > Data Modeling & Design #443 in Books > Computers & Technology > Databases & Big Data > Data Processing
Not sure how useful it will be for me though
Thank you!
A pleasure to read. Very informative and educational. A fresh perspective. One of the better books that I have read in a long time.
The description of the fate of Lake Baykal is nightmarish documentation. Read it at you your own peril. It if frightening!
Natural Language Annotation for Machine Learning Deep Learning: Natural Language Processing in Python with Recursive Neural Networks: Recursive Neural (Tensor) Networks in Theano (Deep Learning and Natural Language Processing Book 3) Deep Learning: Natural Language Processing in Python with GLoVe: From Word2Vec to GLoVe in Python and Theano (Deep Learning and Natural Language Processing) Deep Learning: Natural Language Processing in Python with Word2Vec: Word2Vec and Word Embeddings in Python and Theano (Deep Learning and Natural Language Processing Book 1) Deep Learning: Recurrent Neural Networks in Python: LSTM, GRU, and more RNN machine learning architectures in Python and Theano (Machine Learning in Python) Unsupervised Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python and Theano (Machine Learning in Python) Deep Learning in Python Prerequisites: Master Data Science and Machine Learning with Linear Regression and Logistic Regression in Python (Machine Learning in Python) Convolutional Neural Networks in Python: Master Data Science and Machine Learning with Modern Deep Learning in Python, Theano, and TensorFlow (Machine Learning in Python) Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python, Theano, and TensorFlow (Machine Learning in Python) Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) Unsupervised Machine Learning in Python: Master Data Science and Machine Learning with Cluster Analysis, Gaussian Mixture Models, and Principal Components Analysis Machine Learning with Spark - Tackle Big Data with Powerful Spark Machine Learning Algorithms Foundations of Machine Learning (Adaptive Computation and Machine Learning series) Introduction to Machine Learning (Adaptive Computation and Machine Learning series) Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series) Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning) Natural Gas Trading: From Natural Gas Stocks to Natural Gas Futures- Your Complete, Step-by-Step Guide to Natural Gas Trading First-Time Machine Applique: Learning to Machine Applique in Nine Easy Lessons A collection of Advanced Data Science and Machine Learning Interview Questions Solved in Python and Spark (II): Hands-on Big Data and Machine ... Programming Interview Questions) (Volume 7) Python: Python Programming For Beginners - The Comprehensive Guide To Python Programming: Computer Programming, Computer Language, Computer Science (Machine Language)
