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Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library.Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations.Explore computational graphs and the supervised learning paradigmMaster the basics of the PyTorch optimized tensor manipulation libraryGet an overview of traditional NLP concepts and methodsLearn the basic ideas involved in building neural networksUse embeddings to represent words, sentences, documents, and other featuresExplore sequence prediction and generate sequence-to-sequence modelsLearn design patterns for building production NLP systems

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The Theme of IJCNLP 2005: "NLP with Kimchee", a Conference with a Unique Flavor Welcometo IJCNLP 2005,thesecondannualconferenceof theAsian Federation ofNaturalLanguageProcessing(AFNLP). Followingthesuccessofthe?rstc- ference held in the beautiful cityof Sanya,Hainan Island,China, in March2004, IJCNLP 2005 is held in yet another attractive Asian resort, namely Jeju Island in Korea, on October 11-13, 2005 - the ideal place and season for appreciating mugunghwa, the rose of Sharon, and the national ?ower of Korea. On behalf of the Program Committee, we are excited to present these p- ceedings, which collect together the papers accepted for oral presentation at the conference. We received 289 submissions in total, from 32 economies all over the world: 77% from Asia, 11% from Europe, 0.3% from Africa, 1.7% from Australasia and 10% from North America. We are delighted to report that the popularity of IJCNLP has signi?cantly increased this year, with an increase of 37% from the 211 submissions from 16 economies and 3 continents received for IJCNLP 2004. With such a large number of submissions, the paper selection process was not easy. With the very considerable assistance of our 12 area chairs - Claire Gardent,Jamie Henderson,Chu-Ren Huang,Kentaro Inui, GaryLee, Kim-Teng Lua, Helen Meng, Diego Moll´ a, Jian-Yun Nie, Dragomir Radev, Manfred Stede, andMing Zhou- andthe 133internationalreviewers,90papers(31%)were- cepted for oral presentation and 62 papers (21%) were recommended as posters.

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Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library.Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations.Explore computational graphs and the supervised learning paradigmMaster the basics of the PyTorch optimized tensor manipulation libraryGet an overview of traditional NLP concepts and methodsLearn the basic ideas involved in building neural networksUse embeddings to represent words, sentences, documents, and other featuresExplore sequence prediction and generate sequence-to-sequence modelsLearn design patterns for building production NLP systems

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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.

Jackson, P. / I. Moulinier: Natural Language Processing for Online Applications. Text Retrieval, Extraction and Categorization. Amsterdam, 2002, X 225 P. , 335 Gr. Encuadernacion Original. Nuevo. (lb-1-6) 335 Gr.

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"June 2020: First Edition" and "Revision History For First Edition 2020-06-17: First edition" stated. Near fine plus, if not fine wraps/paperback. Only trivial signs of age/wear/previous use.

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Book is in Used-VeryGood condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear and contain very limited notes and highlighting. 1.49

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