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Author : Dale Dougherty,Tim O'Reilly
Publisher : Unknown
Page : 694 pages
File Size : 41,6 Mb
Release : 1987
Category : Computers
ISBN : UCSC:32106007731190
Author : David Mertz
Publisher : Addison-Wesley Professional
Page : 544 pages
File Size : 55,5 Mb
Release : 2003
Category : Computers
ISBN : 0321112547
bull; Demonstrates how Python is the perfect language for text-processing functions. bull; Provides practical pointers and tips that emphasize efficient, flexible, and maintainable approaches to text-processing challenges. bull; Helps programmers develop solutions for dealing with the increasing amounts of data with which we are all inundated.
Author : Dan Jurafsky
Publisher : Pearson Education India
Page : 912 pages
File Size : 44,8 Mb
Release : 2000-09
Category : Electronic
ISBN : 8131716724
Author : Rob Miller
Publisher : Unknown
Page : 0 pages
File Size : 45,5 Mb
Release : 2015
Category : Ruby (Computer program language)
ISBN : 1680500708
"Whatever you want to do with text, Ruby is up to the job. Most information in the world is in text format, and you need to make sense of the data hiding within. You want to do this efficiently, avoiding labor-intensive, manual work. Text Processing with Ruby takes a practical approach to working with text. First, Aquire: Explore Ruby's core and standard library, and extract text into your Ruby programs. Process delimited files and web pages, and write utilities. Second, Transform: Use regular expressions, write a parser, and use Natural Language Processing techniques. Finally, Load: Write the transformed text and data to standard output, files, and other processes. Serialize text into JSON, XML, and CVS, and use ERB to create more complex formats. You'll soon be able to tackle even the most enormous and entangled text with ease."--Back cover.
Author : Julia Silge,David Robinson
Publisher : "O'Reilly Media, Inc."
Page : 193 pages
File Size : 55,7 Mb
Release : 2017-06-12
Category : Computers
ISBN : 9781491981627
Chapter 7. Case Study : Comparing Twitter Archives; Getting the Data and Distribution of Tweets; Word Frequencies; Comparing Word Usage; Changes in Word Use; Favorites and Retweets; Summary; Chapter 8. Case Study : Mining NASA Metadata; How Data Is Organized at NASA; Wrangling and Tidying the Data; Some Initial Simple Exploration; Word Co-ocurrences and Correlations; Networks of Description and Title Words; Networks of Keywords; Calculating tf-idf for the Description Fields; What Is tf-idf for the Description Field Words?; Connecting Description Fields to Keywords; Topic Modeling.
Author : Jimmy Lin,Chris Dyer
Publisher : Springer Nature
Page : 171 pages
File Size : 43,5 Mb
Release : 2022-05-31
Category : Computers
ISBN : 9783031021367
Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well. Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks
Author : Mitzi Morris
Publisher : Unknown
Page : 328 pages
File Size : 52,5 Mb
Release : 2014-01-01
Category : Electronic
ISBN : 0988208725
This book teaches you how to master the subtle art of multilingual text processing and prevent text data corruption. It provides an introduction to natural language processing using Lucene and Solr. It gives you tools and techniques to manage large collections of text data, whether they come from news feeds, databases, or legacy documents. Each chapter contains executable programs that can also be used for text data forensics. Topics covered: Unicode code points Character encodings from ASCII and Big5 to UTF-8 and UTF-32LE Character normalization using International Components for Unicode (ICU) Java I/O, including working directly with zip, gzip, and tar files Regular expressions in Java Transporting text data via HTTP Parsing and generating XML, HTML, and JSON Using Lucene 4 for natural language search and text classification Search, spelling correction, and clustering with Solr 4 Other books on text processing presuppose much of the material covered in this book. They gloss over the details of transforming text from one format to another and assume perfect input data. The messy reality of raw text will have you reaching for this book again and again.
Author : Francisco M. Couto
Publisher : Springer
Page : 98 pages
File Size : 40,5 Mb
Release : 2019-06-10
Category : Medical
ISBN : 9783030138455
This open access book is a step-by-step introduction on how shell scripting can help solve many of the data processing tasks that Health and Life specialists face everyday with minimal software dependencies. The examples presented in the book show how simple command line tools can be used and combined to retrieve data and text from web resources, to filter and mine literature, and to explore the semantics encoded in biomedical ontologies. To store data this book relies on open standard text file formats, such as TSV, CSV, XML, and OWL, that can be open by any text editor or spreadsheet application. The first two chapters, Introduction and Resources, provide a brief introduction to the shell scripting and describe popular data resources in Health and Life Sciences. The third chapter, Data Retrieval, starts by introducing a common data processing task that involves multiple data resources. Then, this chapter explains how to automate each step of that task by introducing the required commands line tools one by one. The fourth chapter, Text Processing, shows how to filter and analyze text by using simple string matching techniques and regular expressions. The last chapter, Semantic Processing, shows how XPath queries and shell scripting is able to process complex data, such as the graphs used to specify ontologies. Besides being almost immutable for more than four decades and being available in most of our personal computers, shell scripting is relatively easy to learn by Health and Life specialists as a sequence of independent commands. Comprehending them is like conducting a new laboratory protocol by testing and understanding its procedural steps and variables, and combining their intermediate results. Thus, this book is particularly relevant to Health and Life specialists or students that want to easily learn how to process data and text, and which in return may facilitate and inspire them to acquire deeper bioinformatics skills in the future.
Author : Jean Véronis
Publisher : Springer Science & Business Media
Page : 417 pages
File Size : 46,5 Mb
Release : 2013-03-14
Category : Language Arts & Disciplines
ISBN : 9789401725354
l This book evolved from the ARCADE evaluation exercise that started in 1995. The project's goal is to evaluate alignment systems for parallel texts, i. e. , texts accompanied by their translation. Thirteen teams from various places around the world have participated so far and for the first time, some ten to fifteen years after the first alignment techniques were designed, the community has been able to get a clear picture of the behaviour of alignment systems. Several chapters in this book describe the details of competing systems, and the last chapter is devoted to the description of the evaluation protocol and results. The remaining chapters were especially commissioned from researchers who have been major figures in the field in recent years, in an attempt to address a wide range of topics that describe the state of the art in parallel text processing and use. As I recalled in the introduction, the Rosetta stone won eternal fame as the prototype of parallel texts, but such texts are probably almost as old as the invention of writing. Nowadays, parallel texts are electronic, and they are be coming an increasingly important resource for building the natural language processing tools needed in the "multilingual information society" that is cur rently emerging at an incredible speed. Applications are numerous, and they are expanding every day: multilingual lexicography and terminology, machine and human translation, cross-language information retrieval, language learning, etc.
Author : Alexander Gelbukh
Publisher : Springer Science & Business Media
Page : 845 pages
File Size : 42,6 Mb
Release : 2005-01-31
Category : Computers
ISBN : 9783540245230
This book constitutes the refereed proceedings of the 6th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2005, held in Mexico City, Mexico in February 2005. The 53 revised full papers and 35 revised short papers presented together with 4 invited papers were carefully reviewed and selected from 151 submissions. The papers are organized in topical sections on computational linguistics forum; semantics and discourse; parsing and syntactic disambiguation; morphology; anaphora and conference; word sense disambiguation; lexical resources; natural language generation; machine translation; speech and natural language interfaces; language documentation; information extraction, information retrieval; question answering; summarization; text classification, categorization, and clustering; named entity recognition; language identification; and spelling and style checking.
Author : Vivian Siahaan,Rismon Hasiholan Sianipar
Publisher : BALIGE PUBLISHING
Page : 334 pages
File Size : 43,8 Mb
Release : 2023-06-26
Category : Computers
ISBN : 8210379456XXX
In this book, we explored a code implementation for sentiment analysis using machine learning models, including XGBoost, LightGBM, and LSTM. The code aimed to build, train, and evaluate these models on Twitter data to classify sentiments. Throughout the project, we gained insights into the key steps involved and observed the findings and functionalities of the code. Sentiment analysis is a vital task in natural language processing, and the code was to give a comprehensive approach to tackle it. The implementation began by checking if pre-trained models for XGBoost and LightGBM existed. If available, the models were loaded; otherwise, new models were built and trained. This approach allowed for reusability of trained models, saving time and effort in subsequent runs. Similarly, the code checked if preprocessed data for LSTM existed. If not, it performed tokenization and padding on the text data, splitting it into train, test, and validation sets. The preprocessed data was saved for future use. The code also provided a function to build and train the LSTM model. It defined the model architecture using the Keras Sequential API, incorporating layers like embedding, convolutional, max pooling, bidirectional LSTM, dropout, and dense output. The model was compiled with appropriate loss and optimization functions. Training was carried out, with early stopping implemented to prevent overfitting. After training, the model summary was printed, and both the model and training history were saved for future reference. The train_lstm function ensured that the LSTM model was ready for prediction by checking the existence of preprocessed data and trained models. If necessary, it performed the required preprocessing and model building steps. The pred_lstm() function was responsible for loading the LSTM model and generating predictions for the test data. The function returned the predicted sentiment labels, allowing for further analysis and evaluation. To facilitate user interaction, the code included a functionality to choose the LSTM model for prediction. The choose_prediction_lstm() function was triggered when the user selected the LSTM option from a dropdown menu. It called the pred_lstm() function, performed evaluation tasks, and visualized the results. Confusion matrices and true vs. predicted value plots were generated to assess the model's performance. Additionally, the loss and accuracy history from training were plotted, providing insights into the model's learning process. In conclusion, this project provided a comprehensive overview of sentiment analysis using machine learning models. The code implementation showcased the steps involved in building, training, and evaluating models like XGBoost, LightGBM, and LSTM. It emphasized the importance of data preprocessing, model building, and evaluation in sentiment analysis tasks. The code also demonstrated functionalities for reusing pre-trained models and saving preprocessed data, enhancing efficiency and ease of use. Through visualization techniques, such as confusion matrices and accuracy/loss curves, the code enabled a better understanding of the model's performance and learning dynamics. Overall, this project highlighted the practical aspects of sentiment analysis and illustrated how different machine learning models can be employed to tackle this task effectively.
Author : Anne Kao,Steve R. Poteet
Publisher : Springer Science & Business Media
Page : 272 pages
File Size : 40,5 Mb
Release : 2007-03-06
Category : Computers
ISBN : 9781846287541
Natural Language Processing and Text Mining not only discusses applications of Natural Language Processing techniques to certain Text Mining tasks, but also the converse, the use of Text Mining to assist NLP. It assembles a diverse views from internationally recognized researchers and emphasizes caveats in the attempt to apply Natural Language Processing to text mining. This state-of-the-art survey is a must-have for advanced students, professionals, and researchers.
Author : Steven Bird,Ewan Klein,Edward Loper
Publisher : "O'Reilly Media, Inc."
Page : 506 pages
File Size : 42,6 Mb
Release : 2009-06-12
Category : Computers
ISBN : 9780596555719
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.
Author : Gerard Salton
Publisher : Addison Wesley Publishing Company
Page : 552 pages
File Size : 55,5 Mb
Release : 1989
Category : Computers
ISBN : UOM:35128001034329
Author : Daniel Shiffman
Publisher : Newnes
Page : 564 pages
File Size : 44,7 Mb
Release : 2015-09-09
Category : Computers
ISBN : 9780123947925
Learning Processing, Second Edition, is a friendly start-up guide to Processing, a free, open-source alternative to expensive software and daunting programming languages. Requiring no previous experience, this book is for the true programming beginner. It teaches the basic building blocks of programming needed to create cutting-edge graphics applications including interactive art, live video processing, and data visualization. Step-by-step examples, thorough explanations, hands-on exercises, and sample code, supports your learning curve. A unique lab-style manual, the book gives graphic and web designers, artists, and illustrators of all stripes a jumpstart on working with the Processing programming environment by providing instruction on the basic principles of the language, followed by careful explanations of select advanced techniques. The book has been developed with a supportive learning experience at its core. From algorithms and data mining to rendering and debugging, it teaches object-oriented programming from the ground up within the fascinating context of interactive visual media. This book is ideal for graphic designers and visual artists without programming background who want to learn programming. It will also appeal to students taking college and graduate courses in interactive media or visual computing, and for self-study. A friendly start-up guide to Processing, a free, open-source alternative to expensive software and daunting programming languages No previous experience required—this book is for the true programming beginner! Step-by-step examples, thorough explanations, hands-on exercises, and sample code supports your learning curve
