Programming for Corpus Linguistics with Python and Dataframes PDF Download
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Author: Daniel Keller
Publisher: Cambridge University Press
ISBN: 1108916384
Category : Language Arts & Disciplines
Languages : en
Pages : 226
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Book Description
This Element offers intermediate or experienced programmers algorithms for Corpus Linguistic (CL) programming in the Python language using dataframes that provide a fast, efficient, intuitive set of methods for working with large, complex datasets such as corpora. This Element demonstrates principles of dataframe programming applied to CL analyses, as well as complete algorithms for creating concordances; producing lists of collocates, keywords, and lexical bundles; and performing key feature analysis. An additional algorithm for creating dataframe corpora is presented including methods for tokenizing, part-of-speech tagging, and lemmatizing using spaCy. This Element provides a set of core skills that can be applied to a range of CL research questions, as well as to original analyses not possible with existing corpus software.
Author: Daniel Keller
Publisher: Cambridge University Press
ISBN: 1108916384
Category : Language Arts & Disciplines
Languages : en
Pages : 226
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Book Description
This Element offers intermediate or experienced programmers algorithms for Corpus Linguistic (CL) programming in the Python language using dataframes that provide a fast, efficient, intuitive set of methods for working with large, complex datasets such as corpora. This Element demonstrates principles of dataframe programming applied to CL analyses, as well as complete algorithms for creating concordances; producing lists of collocates, keywords, and lexical bundles; and performing key feature analysis. An additional algorithm for creating dataframe corpora is presented including methods for tokenizing, part-of-speech tagging, and lemmatizing using spaCy. This Element provides a set of core skills that can be applied to a range of CL research questions, as well as to original analyses not possible with existing corpus software.
Author: Stefan Th. Gries
Publisher: Taylor & Francis
ISBN: 1317597664
Category : Education
Languages : en
Pages : 287
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Book Description
As in its first edition, the new edition of Quantitative Corpus Linguistics with R demonstrates how to process corpus-linguistic data with the open-source programming language and environment R. Geared in general towards linguists working with observational data, and particularly corpus linguists, it introduces R programming with emphasis on: data processing and manipulation in general; text processing with and without regular expressions of large bodies of textual and/or literary data, and; basic aspects of statistical analysis and visualization. This book is extremely hands-on and leads the reader through dozens of small applications as well as larger case studies. Along with an array of exercise boxes and separate answer keys, the text features a didactic sequential approach in case studies by way of subsections that zoom in to every programming problem. The companion website to the book contains all relevant R code (amounting to approximately 7,000 lines of heavily commented code), most of the data sets as well as pointers to others, and a dedicated Google newsgroup. This new edition is ideal for both researchers in corpus linguistics and instructors who want to promote hands-on approaches to data in corpus linguistics courses.
Author: Jonathan Dunn
Publisher: Cambridge University Press
ISBN: 1009083740
Category : Language Arts & Disciplines
Languages : en
Pages : 149
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Book Description
Corpus analysis can be expanded and scaled up by incorporating computational methods from natural language processing. This Element shows how text classification and text similarity models can extend our ability to undertake corpus linguistics across very large corpora. These computational methods are becoming increasingly important as corpora grow too large for more traditional types of linguistic analysis. We draw on five case studies to show how and why to use computational methods, ranging from usage-based grammar to authorship analysis to using social media for corpus-based sociolinguistics. Each section is accompanied by an interactive code notebook that shows how to implement the analysis in Python. A stand-alone Python package is also available to help readers use these methods with their own data. Because large-scale analysis introduces new ethical problems, this Element pairs each new methodology with a discussion of potential ethical implications.
Author: Steven Bird
Publisher: "O'Reilly Media, Inc."
ISBN: 0596555717
Category : Computers
Languages : en
Pages : 506
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Book Description
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: Jesse Egbert
Publisher: Cambridge University Press
ISBN: 1108897037
Category : Language Arts & Disciplines
Languages : en
Pages : 97
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Book Description
Paradoxically, doing corpus linguistics is both easier and harder than it has ever been before. On the one hand, it is easier because we have access to more existing corpora, more corpus analysis software tools, and more statistical methods than ever before. On the other hand, reliance on these existing corpora and corpus linguistic methods can potentially create layers of distance between the researcher and the language in a corpus, making it a challenge to do linguistics with a corpus. The goal of this Element is to explore ways for us to improve how we approach linguistic research questions with quantitative corpus data. We introduce and illustrate the major steps in the research process, including how to: select and evaluate corpora, establish linguistically-motivated research questions, observational units and variables, select linguistically interpretable variables, understand and evaluate existing corpus software tools, adopt minimally sufficient statistical methods, and qualitatively interpret quantitative findings.
Author: Douglas Biber
Publisher: Cambridge University Press
ISBN: 1316298701
Category : Language Arts & Disciplines
Languages : en
Pages : 757
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Book Description
The Cambridge Handbook of English Corpus Linguistics (CHECL) surveys the breadth of corpus-based linguistic research on English, including chapters on collocations, phraseology, grammatical variation, historical change, and the description of registers and dialects. The most innovative aspects of the CHECL are its emphasis on critical discussion, its explicit evaluation of the state of the art in each sub-discipline, and the inclusion of empirical case studies. While each chapter includes a broad survey of previous research, the primary focus is on a detailed description of the most important corpus-based studies in this area, with discussion of what those studies found, and why they are important. Each chapter also includes a critical discussion of the corpus-based methods employed for research in this area, as well as an explicit summary of new findings and discoveries.
Author: Martin Weisser
Publisher: John Wiley & Sons
ISBN: 1119907942
Category : Computers
Languages : en
Pages : 295
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Book Description
Learn how to use Python for linguistics and digital humanities research, perfect for students working with Python for the first time Python programming is no longer only for computer science students; it is now an essential skill in linguistics, the digital humanities (DH), and social science programs that involve text analytics. Python Programming for Linguistics and Digital Humanities provides a comprehensive introduction to this widely used programming language, offering guidance on using Python to perform various processing and analysis techniques on text. Assuming no prior knowledge of programming, this student-friendly guide covers essential topics and concepts such as installing Python, using the command line, working with strings, writing modular code, designing a simple graphical user interface (GUI), annotating language data in XML and TEI, creating basic visualizations, and more. This invaluable text explains the basic tools students will need to perform their own research projects and tackle various data analysis problems. Throughout the book, hands-on exercises provide students with the opportunity to apply concepts to particular questions or projects in processing textual data and solving language-related issues. Each chapter concludes with a detailed discussion of the code applied, possible alternatives, and potential pitfalls or error messages. Teaches students how to use Python to tackle the types of problems they will encounter in linguistics and the digital humanities Features numerous practical examples of language analysis, gradually moving from simple concepts and programs to more complex projects Describes how to build a variety of data visualizations, such as frequency plots and word clouds Focuses on the text processing applications of Python, including creating word and frequency lists, recognizing linguistic patterns, and processing words for morphological analysis Includes access to a companion website with all Python programs produced in the chapter exercises and additional Python programming resources Python Programming for Linguistics and Digital Humanities: Applications for Text-Focused Fields is a must-have resource for students pursuing text-based research in the humanities, the social sciences, and all subfields of linguistics, particularly computational linguistics and corpus linguistics.
Author: Bodo Winter
Publisher: Routledge
ISBN: 1351677438
Category : Education
Languages : en
Pages : 327
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Book Description
Statistics for Linguists: An Introduction Using R is the first statistics textbook on linear models for linguistics. The book covers simple uses of linear models through generalized models to more advanced approaches, maintaining its focus on conceptual issues and avoiding excessive mathematical details. It contains many applied examples using the R statistical programming environment. Written in an accessible tone and style, this text is the ideal main resource for graduate and advanced undergraduate students of Linguistics statistics courses as well as those in other fields, including Psychology, Cognitive Science, and Data Science.
Author: Laura Igual
Publisher: Springer
ISBN: 3319500171
Category : Computers
Languages : en
Pages : 227
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Book Description
This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.
Author: Dipanjan Sarkar
Publisher: Apress
ISBN: 1484223888
Category : Computers
Languages : en
Pages : 397
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Book Description
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
