Natural Language Processing with Python

Analyzing Text with the Natural Language Toolkit

Author: Steven Bird,Ewan Klein,Edward Loper

Publisher: "O'Reilly Media, Inc."

ISBN: 0596555717

Category: Computers

Page: 504

View: 4710

This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

Python Natural Language Processing

Author: Jalaj Thanaki

Publisher: Packt Publishing Ltd

ISBN: 1787285529

Category: Computers

Page: 486

View: 1190

Leverage the power of machine learning and deep learning to extract information from text data About This Book Implement Machine Learning and Deep Learning techniques for efficient natural language processing Get started with NLTK and implement NLP in your applications with ease Understand and interpret human languages with the power of text analysis via Python Who This Book Is For This book is intended for Python developers who wish to start with natural language processing and want to make their applications smarter by implementing NLP in them. What You Will Learn Focus on Python programming paradigms, which are used to develop NLP applications Understand corpus analysis and different types of data attribute. Learn NLP using Python libraries such as NLTK, Polyglot, SpaCy, Standford CoreNLP and so on Learn about Features Extraction and Feature selection as part of Features Engineering. Explore the advantages of vectorization in Deep Learning. Get a better understanding of the architecture of a rule-based system. Optimize and fine-tune Supervised and Unsupervised Machine Learning algorithms for NLP problems. Identify Deep Learning techniques for Natural Language Processing and Natural Language Generation problems. In Detail This book starts off by laying the foundation for Natural Language Processing and why Python is one of the best options to build an NLP-based expert system with advantages such as Community support, availability of frameworks and so on. Later it 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. During the course of the book, you will explore the semantic as well as syntactic analysis of text. You will understand how to solve various ambiguities in processing human language and will come across various scenarios while performing text analysis. You will learn the very basics of getting the environment ready for natural language processing, move on to the initial setup, and then quickly understand sentences and language parts. You will learn the power of Machine Learning and Deep Learning to extract information from text data. By the end of the book, you will have a clear understanding of natural language processing and will have worked on multiple examples that implement NLP in the real world. Style and approach This book teaches the readers various aspects of natural language Processing using NLTK. It takes the reader from the basic to advance level in a smooth way.

Hands-On Natural Language Processing with Python

A practical guide to applying deep learning architectures to your NLP applications

Author: Rajesh Arumugam,Rajalingappaa Shanmugamani

Publisher: Packt Publishing Ltd

ISBN: 1789135915

Category: Computers

Page: 312

View: 3645

Foster your NLP applications with the help of deep learning, NLTK, and TensorFlow Key Features Weave neural networks into linguistic applications across various platforms Perform NLP tasks and train its models using NLTK and TensorFlow Boost your NLP models with strong deep learning architectures such as CNNs and RNNs Book Description Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges. To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection. You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow. By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts. What you will learn Implement semantic embedding of words to classify and find entities Convert words to vectors by training in order to perform arithmetic operations Train a deep learning model to detect classification of tweets and news Implement a question-answer model with search and RNN models Train models for various text classification datasets using CNN Implement WaveNet a deep generative model for producing a natural-sounding voice Convert voice-to-text and text-to-voice Train a model to convert speech-to-text using DeepSpeech Who this book is for Hands-on Natural Language Processing with Python is for you if you are a developer, machine learning or an NLP engineer who wants to build a deep learning application that leverages NLP techniques. This comprehensive guide is also useful for deep learning users who want to extend their deep learning skills in building NLP applications. All you need is the basics of machine learning and Python to enjoy the book.

Natural Language Processing with Python Cookbook

Over 60 recipes to implement text analytics solutions using deep learning principles

Author: Krishna Bhavsar,Naresh Kumar,Pratap Dangeti

Publisher: Packt Publishing Ltd

ISBN: 178728347X

Category: Computers

Page: 316

View: 8231

Learn the tricks and tips that will help you design Text Analytics solutions About This Book Independent recipes that will teach you how to efficiently perform Natural Language Processing in Python Use dictionaries to create your own named entities using this easy-to-follow guide Learn how to implement NLTK for various scenarios with the help of example-rich recipes to take you beyond basic Natural Language Processing Who This Book Is For This book is intended for data scientists, data analysts, and data science professionals who want to upgrade their existing skills to implement advanced text analytics using NLP. Some basic knowledge of Natural Language Processing is recommended. What You Will Learn Explore corpus management using internal and external corpora Learn WordNet usage and a couple of simple application assignments using WordNet Operate on raw text Learn to perform tokenization, stemming, lemmatization, and spelling corrections, stop words removals, and more Understand regular expressions for pattern matching Learn to use and write your own POS taggers and grammars Learn to evaluate your own trained models Explore Deep Learning techniques in NLP Generate Text from Nietzsche's writing using LSTM Utilize the BABI dataset and LSTM to model episodes In Detail Natural Language Processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages; in particular, it's about programming computers to fruitfully process large natural language corpora. This book includes unique recipes that will teach you various aspects of performing Natural Language Processing with NLTK—the leading Python platform for the task. You will come across various recipes during the course, covering (among other topics) natural language understanding, Natural Language Processing, and syntactic analysis. You will learn how to understand language, plan sentences, and work around various ambiguities. You will learn how to efficiently use NLTK and implement text classification, identify parts of speech, tag words, and more. You will also learn how to analyze sentence structures and master lexical analysis, syntactic and semantic analysis, pragmatic analysis, and the application of deep learning techniques. By the end of this book, you will have all the knowledge you need to implement Natural Language Processing with Python. Style and Approach This book's rich collection of recipes will come in handy when you are working with Natural Language Processing with Python. Addressing your common and not-so-common pain points, this is a book that you must have on the shelf.

Natural Language Processing: Python and NLTK

Author: Nitin Hardeniya,Jacob Perkins,Deepti Chopra,Nisheeth Joshi,Iti Mathur

Publisher: Packt Publishing Ltd

ISBN: 178728784X

Category: Computers

Page: 687

View: 1703

Learn to build expert NLP and machine learning projects using NLTK and other Python libraries About This Book Break text down into its component parts for spelling correction, feature extraction, and phrase transformation Work through NLP concepts with simple and easy-to-follow programming recipes Gain insights into the current and budding research topics of NLP Who This Book Is For If you are an NLP or machine learning enthusiast and an intermediate Python programmer who wants to quickly master NLTK for natural language processing, then this Learning Path will do you a lot of good. Students of linguistics and semantic/sentiment analysis professionals will find it invaluable. What You Will Learn The scope of natural language complexity and how they are processed by machines Clean and wrangle text using tokenization and chunking to help you process data better Tokenize text into sentences and sentences into words Classify text and perform sentiment analysis Implement string matching algorithms and normalization techniques Understand and implement the concepts of information retrieval and text summarization Find out how to implement various NLP tasks in Python In Detail Natural Language Processing is a field of computational linguistics and artificial intelligence that deals 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. The number of human-computer interaction instances are increasing so it's becoming imperative that computers comprehend all major natural languages. The first NLTK Essentials module is an introduction on how to build systems around NLP, with a focus on how to create a customized tokenizer and parser from scratch. You will learn essential concepts of NLP, be given practical insight into open source tool and libraries available in Python, shown how to analyze social media sites, and be given tools to deal with large scale text. This module also provides a workaround using some of the amazing capabilities of Python libraries such as NLTK, scikit-learn, pandas, and NumPy. The second Python 3 Text Processing with NLTK 3 Cookbook module teaches you the essential techniques of text and language processing with simple, straightforward examples. This includes organizing text corpora, creating your own custom corpus, text classification with a focus on sentiment analysis, and distributed text processing methods. The third Mastering Natural Language Processing with Python module will help you become an expert and assist you in creating your own NLP projects using NLTK. You will be guided through model development with machine learning tools, shown how to create training data, and given insight into the best practices for designing and building NLP-based applications using Python. This Learning Path combines some of the best that Packt has to offer in one complete, curated package and is designed to help you quickly learn text processing with Python and NLTK. It includes content from the following Packt products: NTLK essentials by Nitin Hardeniya Python 3 Text Processing with NLTK 3 Cookbook by Jacob Perkins Mastering Natural Language Processing with Python by Deepti Chopra, Nisheeth Joshi, and Iti Mathur Style and approach This comprehensive course creates a smooth learning path that teaches you how to get started with Natural Language Processing using Python and NLTK. You'll learn to create effective NLP and machine learning projects using Python and NLTK.

Applied Natural Language Processing with Python

Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing

Author: Taweh Beysolow II

Publisher: Apress

ISBN: 9781484237328

Category: Computers

Page: 124

View: 3813

Learn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document classification, and natural language generation. Along the way, you will learn the skills to implement these methods in larger infrastructures to replace existing code or create new algorithms. Applied Natural Language Processing with Python starts with reviewing the necessary machine learning concepts before moving onto discussing various NLP problems. After reading this book, you will have the skills to apply these concepts in your own professional environment. What You Will Learn Utilize various machine learning and natural language processing libraries such as TensorFlow, Keras, NLTK, and Gensim Manipulate and preprocess raw text data in formats such as .txt and .pdf Strengthen your skills in data science by learning both the theory and the application of various algorithms Who This Book Is For You should be at least a beginner in ML to get the most out of this text, but you needn’t feel that you need be an expert to understand the content.

Mastering Natural Language Processing with Python

Author: Deepti Chopra,Nisheeth Joshi,Iti Mathur

Publisher: Packt Publishing Ltd

ISBN: 178398905X

Category: Computers

Page: 238

View: 9015

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.

Deep Learning for Natural Language Processing

Creating Neural Networks with Python

Author: Palash Goyal,Sumit Pandey,Karan Jain

Publisher: Apress

ISBN: 1484236858

Category: Computers

Page: 277

View: 5749

Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system. This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways. What You Will Learn Gain the fundamentals of deep learning and its mathematical prerequisites Discover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classification Who This Book Is For Software developers who are curious to try out deep learning with NLP.

Natural Language Processing with Python

Natural Language Processing Using Nltk

Author: Frank Millstein

Publisher: Createspace Independent Publishing Platform

ISBN: 9781986466356

Category:

Page: 120

View: 2285

Natural Language Processing With Python This book is a perfect beginner's guide to natural language processing. It is offering an easy to understand guide to implementing NLP techniques using Python. Natural language processing has been around for more than fifty years, but just recently with greater amounts of data present and better computational powers, it has gained a greater popularity. Given the importance of data, there is no wonder why natural language processing is on the rise. If you are interested in learning more, this book will serve as your best companion on this journey introducing you to this challenging, yet extremely engaging world of automatic manipulation of our human language. It covers all the basics you need to know before you dive deeper into NLP and solving more complex NLP tasks in Python. Here Is a Preview of What You'll Learn Here... The main challenges of natural language processing The history of natural language processing How natural langauge processing actually works The main natural language processing applications Text preprocessing and noise removal Feature engineering and syntactic parsing Part of speech tagging and named entity extraction Topic modeling and word embedding Text classification problems Working with text data using NLTK Text summarization and sentiment analysis And much, much more...

Deep Learning with Text

A Modern Approach to Natural Language Processing with Python and Keras

Author: Patrick Harrison,Matthew Honnibal

Publisher: O'Reilly Media

ISBN: 9781491984413

Category: Computers

Page: 250

View: 3935

The advent of deep learning has been transformative for many difficult problems in machine learning, often delivering breakthrough performance compared with previous techniques. This paradigm shift has swept over the field of natural language processing, where an emerging deep learning approach has set the state-of-the-art in text categorization, information extraction, recommendations, and more. Deep Learning with Text is a practitioner's guide that will help you learn how the neural networks that power modern natural language processing techniques work "under the hood." You'll find examples using "batteries-included" libraries in Python--including spaCy, gensim, and others--for applying this modern, deep learning approach to solve real-world problems with natural language text. Until now, much of the published material about deep learning has been sequestered in research papers and graduate-level academic textbooks. This practical book will enable software developers and data scientists to build new products and systems that have only become possible in the past couple of years.

Natural Language Processing with TensorFlow

Teach language to machines using Python's deep learning library

Author: Thushan Ganegedara

Publisher: Packt Publishing Ltd

ISBN: 1788477758

Category: Computers

Page: 472

View: 5764

Write modern natural language processing applications using deep learning algorithms and TensorFlow Key Features Focuses on more efficient natural language processing using TensorFlow Covers NLP as a field in its own right to improve understanding for choosing TensorFlow tools and other deep learning approaches Provides choices for how to process and evaluate large unstructured text datasets Learn to apply the TensorFlow toolbox to specific tasks in the most interesting field in artificial intelligence Book Description Natural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today’s data streams, and apply these tools to specific NLP tasks. Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You'll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply high-performance RNN models, like long short-term memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator. After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks. What you will learn Core concepts of NLP and various approaches to natural language processing How to solve NLP tasks by applying TensorFlow functions to create neural networks Strategies to process large amounts of data into word representations that can be used by deep learning applications Techniques for performing sentence classification and language generation using CNNs and RNNs About employing state-of-the art advanced RNNs, like long short-term memory, to solve complex text generation tasks How to write automatic translation programs and implement an actual neural machine translator from scratch The trends and innovations that are paving the future in NLP Who this book is for This book is for Python developers with a strong interest in deep learning, who want to learn how to leverage TensorFlow to simplify NLP tasks. Fundamental Python skills are assumed, as well as some knowledge of machine learning and undergraduate-level calculus and linear algebra. No previous natural language processing experience required, although some background in NLP or computational linguistics will be helpful.

Natural Language Processing in Action

Author: Hobson Lane,Cole Howard,Hannes Hapke

Publisher: Pearson Professional

ISBN: 9781617294631

Category: Computers

Page: 420

View: 5630

Modern NLP techniques based on machine learning radically improve the ability of software to recognize patterns, use context to infer meaning, and accurately discern intent from poorly-structured text. In Natural Language Processing in Action, readers explore carefully-chosen examples and expand their machine's knowledge which they can then apply to a range of challenges. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Python 3 Text Processing with NLTK 3 Cookbook

Author: Jacob Perkins

Publisher: Packt Publishing Ltd

ISBN: 1782167862

Category: Computers

Page: 304

View: 2645

This book is intended for Python programmers interested in learning how to do natural language processing. Maybe you’ve learned the limits of regular expressions the hard way, or you’ve realized that human language cannot be deterministically parsed like a computer language. Perhaps you have more text than you know what to do with, and need automated ways to analyze and structure that text. This Cookbook will show you how to train and use statistical language models to process text in ways that are practically impossible with standard programming tools. A basic knowledge of Python and the basic text processing concepts is expected. Some experience with regular expressions will also be helpful.

Text Analytics with Python

A Practical Real-World Approach to Gaining Actionable Insights from your Data

Author: Dipanjan Sarkar

Publisher: Apress

ISBN: 1484223888

Category: Computers

Page: 385

View: 366

Derive useful insights from your data using Python. You will learn both basic and advanced concepts, including text and language syntax, structure, and semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. Text Analytics with Python teaches you the techniques related to natural language processing and text analytics, and you will gain the skills to know which technique is best suited to solve a particular problem. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems. What You Will Learn: Understand the major concepts and techniques of natural language processing (NLP) and text analytics, including syntax and structure Build a text classification system to categorize news articles, analyze app or game reviews using topic modeling and text summarization, and cluster popular movie synopses and analyze the sentiment of movie reviews Implement Python and popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern Who This Book Is For : IT professionals, analysts, developers, linguistic experts, data scientists, and anyone with a keen interest in linguistics, analytics, and generating insights from textual data

Foundations of Statistical Natural Language Processing

Author: Christopher D.. Manning,Christopher D. Manning,Hinrich Schütze

Publisher: MIT Press

ISBN: 9780262133609

Category: Language Arts & Disciplines

Page: 680

View: 2744

An introduction to statistical natural language processing (NLP). The text contains the theory and algorithms needed for building NLP tools. Topics covered include: mathematical and linguistic foundations; statistical methods; collocation finding; word sense disambiguation; and probalistic parsing.

Discourse Processing

Author: Manfred Stede

Publisher: Morgan & Claypool Publishers

ISBN: 1608457346

Category: Computers

Page: 155

View: 8482

Discourse Processing here is framed as marking up a text with structural descriptions on several levels, which can serve to support many language-processing or text-mining tasks. We first explore some ways of assigning structure on the document level: the logical document structure as determined by the layout of the text, its genre-specific content structure, and its breakdown into topical segments. Then the focus moves to phenomena of local coherence. We introduce the problem of coreference and look at methods for building chains of coreferring entities in the text. Next, the notion of coherence relation is introduced as the second important factor of local coherence. We study the role of connectives and other means of signaling such relations in text, and then return to the level of larger textual units, where tree or graph structures can be ascribed by recursively assigning coherence relations. Taken together, these descriptions can inform text summarization, information extraction, discourse-aware sentiment analysis, question answering, and the like. Table of Contents: Introduction / Large Discourse Units and Topics / Coreference Resolution / Small Discourse Units and Coherence Relations / Summary: Text Structure on Multiple Interacting Levels

Natural Language Processing with Pytorch

Build Intelligent Language Applications Using Deep Learning

Author: Delip Rao,Goku Mohandas

Publisher: N.A

ISBN: 9781491978238

Category: Computers

Page: 250

View: 1635

Natural Language Processing (NLP) offers unbounded opportunities for solving interesting problems in artificial intelligence, making it the latest frontier for developing intelligent, deep learning-based applications. If you're a developer or researcher ready to dive deeper into this rapidly growing area of artificial intelligence, this practical book shows you how to use the PyTorch deep learning framework to implement recently discovered NLP techniques. To get started, all you need is a machine learning background and experience programming with Python. Authors Delip Rao and Goku Mohandas provide you with a solid grounding in PyTorch, and deep learning algorithms, for building applications involving semantic representation of text. Each chapter includes several code examples and illustrations. Get extensive introductions to NLP, deep learning, and PyTorch Understand traditional NLP methods, including NLTK, SpaCy, and gensim Explore embeddings: high quality representations for words in a language Learn representations from a language sequence, using the Recurrent Neural Network (RNN) Improve on RNN results with complex neural architectures, such as Long Short Term Memories (LSTM) and Gated Recurrent Units Explore sequence-to-sequence models (used in translation) that read one sequence and produce another

Applied Natural Language Processing with Python

Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing

Author: Taweh Beysolow II

Publisher: Apress

ISBN: 1484237331

Category: Computers

Page: 150

View: 4621

Learn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document classification, and natural language generation. Along the way, you will learn the skills to implement these methods in larger infrastructures to replace existing code or create new algorithms. Applied Natural Language Processing with Python starts with reviewing the necessary machine learning concepts before moving onto discussing various NLP problems. After reading this book, you will have the skills to apply these concepts in your own professional environment. What You Will Learn Utilize various machine learning and natural language processing libraries such as TensorFlow, Keras, NLTK, and Gensim Manipulate and preprocess raw text data in formats such as .txt and .pdf Strengthen your skills in data science by learning both the theory and the application of various algorithms Who This Book Is For You should be at least a beginner in ML to get the most out of this text, but you needn’t feel that you need be an expert to understand the content.

Linguistic Databases

Author: John A. Nerbonne

Publisher: Center for the Study of Language and Information Publications

ISBN: 9781575860930

Category: Language Arts & Disciplines

Page: 255

View: 561

Linguistic Databases explores the increasing use of databases in linguistics. The enormous potential in linguistic data - billions of utterances and messages daily - has been difficult to exploit. Many linguists have had to concentrate on introspective data with its inevitable blinders toward frequency, variation, and naturalness. Applications of linguistics have been handicapped. This volume explores the potential advantages of database applications to linguistics. Included in this volume are reports on database activities in phonetics, phonology, lexicography and syntax, comparative grammar, second-language acquisition, linguistic fieldwork, and language pathology. The book presents the specialized problems of multi-media (especially audio) and multi-lingual texts, including those in exotic writing systems. Implemented solutions are also discussed. The opportunities to use existing, minimally structured text repositories are presented.