Natural Language Processing And Text Mining
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Natural Language Processing And Text Mining
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Author : Anne Kao
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-03-06
Natural Language Processing And Text Mining written by Anne Kao and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-03-06 with Computers categories.
The topic this book addresses originated from a panel discussion at the 2004 ACM SIGKDD (Special Interest Group on Knowledge Discovery and Data Mining) Conference held in Seattle, Washington, USA. We the editors or- nized the panel to promote discussion on how text mining and natural l- guageprocessing,tworelatedtopicsoriginatingfromverydi?erentdisciplines, can best interact with each other, and bene?t from each other’s strengths. It attracted a great deal of interest and was attended by 200 people from all over the world. We then guest-edited a special issue of ACM SIGKDD Exp- rations on the same topic, with a number of very interesting papers. At the same time, Springer believed this to be a topic of wide interest and expressed an interest in seeing a book published. After a year of work, we have put - gether 11 papers from international researchers on a range of techniques and applications. We hope this book includes papers readers do not normally ?nd in c- ference proceedings, which tend to focus more on theoretical or algorithmic breakthroughs but are often only tried on standard test data. We would like to provide readers with a wider range of applications, give some examples of the practical application of algorithms on real-world problems, as well as share a number of useful techniques.
Text Analytics With Python
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Author : Dipanjan Sarkar
language : en
Publisher:
Release Date : 2019
Text Analytics With Python written by Dipanjan Sarkar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Artificial intelligence categories.
Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. The second edition of this book will show you how to use the latest state-of-the-art frameworks in NLP, coupled with Machine Learning and Deep Learning to solve real-world case studies leveraging the power of Python. This edition has gone through a major revamp introducing several major changes and new topics based on the recent trends in NLP. We have a dedicated chapter around Python for NLP covering fundamentals on how to work with strings and text data along with introducing the current state-of-the-art open-source frameworks in NLP. We have a dedicated chapter on feature engineering representation methods for text data including both traditional statistical models and newer deep learning based embedding models. Techniques around parsing and processing text data have also been improved with some new methods. Considering popular NLP applications, for text classification, we also cover methods for tuning and improving our models. Text Summarization has gone through a major overhaul in the context of topic models where we showcase how to build, tune and interpret topic models in the context of an interest dataset on NIPS conference papers. Similarly, we cover text similarity techniques with a real-world example of movie recommenders. Sentiment Analysis is covered in-depth with both supervised and unsupervised techniques. We also cover both machine learning and deep learning models for supervised sentiment analysis. Semantic Analysis gets its own dedicated chapter where we also showcase how you can build your own Named Entity Recognition (NER) system from scratch. To conclude things, we also have a completely new chapter on the promised of Deep Learning for NLP where we also showcase a hands-on example on deep transfer learning. While the overall structure of the book remains the same, the entire code base, modules, and chapters will be updated to the latest Python 3.x release. -- Also the key selling points • Implementations are based on Python 3.x and state-of-the-art popular open source libraries in NLP • Covers Machine Learning and Deep Learning for Advanced Text Analytics and NLP • Showcases diverse NLP applications including Classification, Clustering, Similarity Recommenders, Topic Models, Sentiment and Semantic Analysis.
Machine Learning And Data Mining In Pattern Recognition
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Author : Petra Perner
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-07-21
Machine Learning And Data Mining In Pattern Recognition written by Petra Perner and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-21 with Computers categories.
There is no royal road to science, and only those who do not dread the fatiguing climb of its steep paths have a chance of gaining its luminous summits. Karl Marx A Universial Genius of the 19th Century Many scientists from all over the world during the past two years since the MLDM 2007 have come along on the stony way to the sunny summit of science and have worked hard on new ideas and applications in the area of data mining in pattern r- ognition. Our thanks go to all those who took part in this year's MLDM. We appre- ate their submissions and the ideas shared with the Program Committee. We received over 205 submissions from all over the world to the International Conference on - chine Learning and Data Mining, MLDM 2009. The Program Committee carefully selected the best papers for this year’s program and gave detailed comments on each submitted paper. There were 63 papers selected for oral presentation and 17 papers for poster presentation. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data-mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining. Among these topics this year were special contributions to subtopics such as attribute discre- zation and data preparation, novelty and outlier detection, and distances and simila- ties.
Natural Language Processing For Online Applications
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Author : Peter Jackson
language : en
Publisher: John Benjamins Publishing
Release Date : 2007-06-05
Natural Language Processing For Online Applications written by Peter Jackson and has been published by John Benjamins Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-06-05 with Computers categories.
This text covers the technologies of document retrieval, information extraction, and text categorization in a way which highlights commonalities in terms of both general principles and practical concerns. It assumes some mathematical background on the part of the reader, but the chapters typically begin with a non-mathematical account of the key issues. Current research topics are covered only to the extent that they are informing current applications; detailed coverage of longer term research and more theoretical treatments should be sought elsewhere. There are many pointers at the ends of the chapters that the reader can follow to explore the literature. However, the book does maintain a strong emphasis on evaluation in every chapter both in terms of methodology and the results of controlled experimentation.
Text Mining With R
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Author : Julia Silge
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-06-12
Text Mining With R written by Julia Silge and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-12 with Computers categories.
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 using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media. Learn how to apply the tidy text format to NLP Use sentiment analysis to mine the emotional content of text Identify a document’s most important terms with frequency measurements Explore relationships and connections between words with the ggraph and widyr packages Convert back and forth between R’s tidy and non-tidy text formats Use topic modeling to classify document collections into natural groups Examine case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages
Applied Text Analysis With Python
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Author : Benjamin Bengfort
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2018-06-11
Applied Text Analysis With Python written by Benjamin Bengfort and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-11 with Computers categories.
From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems. Preprocess and vectorize text into high-dimensional feature representations Perform document classification and topic modeling Steer the model selection process with visual diagnostics Extract key phrases, named entities, and graph structures to reason about data in text Build a dialog framework to enable chatbots and language-driven interaction Use Spark to scale processing power and neural networks to scale model complexity
Speech And Language Processing
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Author : Daniel Jurafsky
language : en
Publisher:
Release Date : 2000-01
Speech And Language Processing written by Daniel Jurafsky and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-01 with Automatic speech recognition categories.
This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing.
Text Analysis Pipelines
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Author : Henning Wachsmuth
language : en
Publisher: Springer
Release Date : 2015-12-02
Text Analysis Pipelines written by Henning Wachsmuth and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-02 with Computers categories.
This monograph proposes a comprehensive and fully automatic approach to designing text analysis pipelines for arbitrary information needs that are optimal in terms of run-time efficiency and that robustly mine relevant information from text of any kind. Based on state-of-the-art techniques from machine learning and other areas of artificial intelligence, novel pipeline construction and execution algorithms are developed and implemented in prototypical software. Formal analyses of the algorithms and extensive empirical experiments underline that the proposed approach represents an essential step towards the ad-hoc use of text mining in web search and big data analytics. Both web search and big data analytics aim to fulfill peoples’ needs for information in an adhoc manner. The information sought for is often hidden in large amounts of natural language text. Instead of simply returning links to potentially relevant texts, leading search and analytics engines have started to directly mine relevant information from the texts. To this end, they execute text analysis pipelines that may consist of several complex information-extraction and text-classification stages. Due to practical requirements of efficiency and robustness, however, the use of text mining has so far been limited to anticipated information needs that can be fulfilled with rather simple, manually constructed pipelines.
Text Data Mining
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Author : Chengqing Zong
language : en
Publisher: Springer Nature
Release Date : 2021-05-22
Text Data Mining written by Chengqing Zong and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-22 with Computers categories.
This book discusses various aspects of text data mining. Unlike other books that focus on machine learning or databases, it approaches text data mining from a natural language processing (NLP) perspective. The book offers a detailed introduction to the fundamental theories and methods of text data mining, ranging from pre-processing (for both Chinese and English texts), text representation and feature selection, to text classification and text clustering. It also presents the predominant applications of text data mining, for example, topic modeling, sentiment analysis and opinion mining, topic detection and tracking, information extraction, and automatic text summarization. Bringing all the related concepts and algorithms together, it offers a comprehensive, authoritative and coherent overview. Written by three leading experts, it is valuable both as a textbook and as a reference resource for students, researchers and practitioners interested in text data mining. It can also be used for classes on text data mining or NLP.