Steven Bird is Associate Professor in the Department of Computer Science and Software Engineering at the University of Melbourne, and Senior Research Associate in the Linguistic Data Consortium at the University of Pennsylvania. He completed a PhD on computational phonology at the University of Edinburgh in 1990, supervised by Ewan Klein. He later moved to Cameroon to conduct linguistic fieldwork on the Grassfields Bantu languages under the auspices of the Summer Institute of Linguistics. More recently, he spent several years as Associate Director of the Linguistic Data Consortium where he led an R&D team to create models and tools for large databases of annotated text. At Melbourne University, he established a language technology research group and has taught at all levels of the undergraduate computer science curriculum. In 2009, Steven is President of the Association for Computational Linguistics.Ewan Klein is Professor of Language Technology in the School of Informatics at the University of Edinburgh. He completed a PhD on formal semantics at the University of Cambridge in 1978. After some years working at the Universities of Sussex and Newcastle upon Tyne, Ewan took up a teaching position at Edinburgh. He was involved in the establishment of Edinburgh's Language Technology Group in 1993, and has been closely associated with it ever since. From 2000 2002, he took leave from the University to act as Research Manager for the Edinburgh-based Natural Language Research Group of Edify Corporation, Santa Clara, and was responsible for spoken dialogue processing. Ewan is a past President of the European Chapter of the Association for Computational Linguistics and was a founding member and Coordinator of the European Network of Excellence in Human Language Technologies (ELSNET).Edward Loper has recently completed a PhD on machine learning for natural language processing at the the University of Pennsylvania. Edward was a student in Steven's graduate course on computational linguistics in the fall of 2000, and went on to be a TA and share in the development of NLTK. In addition to NLTK, he has helped develop two packages for documenting and testing Python software, epydoc, and doctest.

InhaltsverzeichnisChapter 1 Language Processing and PythonComputing with Language: Texts and WordsA Closer Look at Python: Texts as Lists of WordsComputing with Language: Simple StatisticsBack to Python: Making Decisions and Taking ControlAutomatic Natural Language UnderstandingSummaryFurther ReadingExercisesChapter 2 Accessing Text Corpora and LexicalResourcesAccessing Text CorporaConditional Frequency DistributionsMore Python: Reusing CodeLexical ResourcesWordNetSummaryFurther ReadingExercisesChapter 3 Processing Raw TextAccessing Text from the Web and from DiskStrings: Text Processing at the Lowest LevelText Processing with UnicodeRegular Expressions for Detecting Word PatternsUseful Applications of Regular ExpressionsNormalizing TextRegular Expressions for Tokenizing TextSegmentationFormatting: From Lists to StringsSummaryFurther ReadingExercisesChapter 4 Writing Structured ProgramsBack to the BasicsSequencesQuestions of StyleFunctions: The Foundation of Structured ProgrammingDoing More with FunctionsProgram DevelopmentAlgorithm DesignA Sample of Python LibrariesSummaryFurther ReadingExercisesChapter 5 Categorizing and Tagging WordsUsing a TaggerTagged CorporaMapping Words to Properties Using Python DictionariesAutomatic TaggingN-Gram TaggingTransformation-Based TaggingHow to Determine the Category of a WordSummaryFurther ReadingExercisesChapter 6 Learning to Classify TextSupervised ClassificationFurther Examples of Supervised ClassificationEvaluationDecision TreesNaive Bayes ClassifiersMaximum Entropy ClassifiersModeling Linguistic PatternsSummaryFurther ReadingExercisesChapter 7 Extracting Information from TextInformation Extraction ChunkingDeveloping and Evaluating ChunkersRecursion in Linguistic StructureNamed Entity RecognitionRelation ExtractionSummaryFurther ReadingExercisesChapter 8 Analyzing Sentence StructureSome Grammatical DilemmasWhat s the Use of Syntax?Context-Free GrammarParsing with Context-Free GrammarDependencies and Dependency GrammarGrammar DevelopmentSummaryFurther ReadingExercisesChapter 9 Building Feature-Based GrammarsGrammatical FeaturesProcessing Feature StructuresExtending a Feature-Based GrammarSummaryFurther ReadingExercisesChapter 10 Analyzing the Meaning of SentencesNatural Language UnderstandingPropositional LogicFirst-Order LogicThe Semantics of English SentencesDiscourse SemanticsSummaryFurther ReadingExercisesChapter 11 Managing Linguistic DataCorpus Structure: A Case StudyThe Life Cycle of a CorpusAcquiring DataWorking with XMLWorking with Toolbox DataDescribing Language Resources Using OLAC MetadataSummaryFurther ReadingExercisesAppendix Afterword: The Language ChallengeLanguage Processing Versus Symbol ProcessingContemporary Philosophical DividesNLTK RoadmapEnvoi...Appendix BibliographyNLTK IndexGeneral IndexColophon

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 analysisAccess popular linguistic databases, including WordNet and treebanksIntegrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis 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.