{"title":"Synthesis Lectures On Human Language Technologies","description":"\u003cp\u003eDive into the world of Human Language Technologies with this insightful collection. From speech recognition to machine translation, explore the cutting edge of computational linguistics and discover the future of communication.\u003c\/p\u003e","products":[{"product_id":"neural-network-methods-in-natural-language-processing-book-yoav-goldberg-9781627052986","title":"Neural Network Methods in Natural Language Processing","description":"Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book covers the basics of supervised machine learning and feed-forward neural networks. 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It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online. Table of Contents: Preface \/ Sentiment Analysis: A Fascinating Problem \/ The Problem of Sentiment Analysis \/ Document Sentiment Classification \/ Sentence Subjectivity and Sentiment Classification \/ Aspect-Based Sentiment Analysis \/ Sentiment Lexicon Generation \/ Opinion Summarization \/ Analysis of Comparative Opinions \/ Opinion Search and Retrieval \/ Opinion Spam Detection \/ Quality of Reviews \/ Concluding Remarks \/ Bibliography \/ Author Biography","brand":"WoB","offers":[{"title":"GB \/ VERY_GOOD \/ INTERNAL","offer_id":49981119037713,"sku":"GOR012284663","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"US \/ GOOD \/ SBYB","offer_id":50264014225681,"sku":"CIN1608458849G","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/1608458849.jpg?v=1750927183"},{"product_id":"introduction-to-linguistic-annotation-and-text-analytics-book-graham-wilcock-9781598297386","title":"Introduction to Linguistic Annotation and Text Analytics","description":"Linguistic annotation and text analytics are active areas of research and development, with academic conferences and industry events such as the Linguistic Annotation Workshops and the annual Text Analytics Summits. This book provides a basic introduction to both fields, and aims to show that good linguistic annotations are the essential foundation for good text analytics. After briefly reviewing the basics of XML, with practical exercises illustrating in-line and stand-off annotations, a chapter is devoted to explaining the different levels of linguistic annotations. The reader is encouraged to create example annotations using the WordFreak linguistic annotation tool. The next chapter shows how annotations can be created automatically using statistical NLP tools, and compares two sets of tools, the OpenNLP and Stanford NLP tools. The second half of the book describes different annotation formats and gives practical examples of how to interchange annotations between different formats using XSLT transformations. The two main text analytics architectures, GATE and UIMA, are then described and compared, with practical exercises showing how to configure and customize them. The final chapter is an introduction to text analytics, describing the main applications and functions including named entity recognition, coreference resolution and information extraction, with practical examples using both open source and commercial tools. Copies of the example files, scripts, and stylesheets used in the book are available from the companion website, located at http: \/\/sites.morganclaypool.com\/wilcock. Table of Contents: Working with XML \/ Linguistic Annotation \/ Using Statistical NLP Tools \/ Annotation Interchange \/ Annotation Architectures \/ Text Analytics","brand":"WoB","offers":[{"title":"GB \/ VERY_GOOD \/ INTERNAL","offer_id":50032275816721,"sku":"GOR007718561","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/B0082ON79C.jpg?v=1750991516"},{"product_id":"learning-to-rank-for-information-retrieval-and-natural-language-processing-book-hang-li-9781627055840","title":"Learning to Rank for Information Retrieval and Natural Language Processing","description":"Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. 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Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet \u0026amp; ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Learning to Rank \/ Learning for Ranking Creation \/ Learning for Ranking Aggregation \/ Methods of Learning to Rank \/ Applications of Learning to Rank \/ Theory of Learning to Rank \/ Ongoing and Future Work","brand":"WoB","offers":[{"title":"US \/ GOOD \/ SBYB","offer_id":50275236970769,"sku":"CIN1627055843G","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/1627055843.jpg?v=1751120503"},{"product_id":"data-intensive-text-processing-with-mapreduce-book-jimmy-lin-9781608453429","title":"Data-Intensive Text Processing with MapReduce","description":"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. 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This book not only intends to help the reader think in MapReduce, but also discusses limitations of the programming model as well. 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Table of Contents: Acknowledgments \/ Introduction\/motivation \/ Morphology: Introduction \/ Morphophonology \/ Morphosyntax \/ Syntax: Introduction \/ Parts of speech \/ Heads, arguments, and adjuncts \/ Argument types and grammatical functions \/ Mismatches between syntactic position and semantic roles \/ Resources \/ Bibliography \/ Author's Biography \/ General Index \/ Index of Languages","brand":"WoB","offers":[{"title":"US \/ VERY_GOOD \/ SBYB","offer_id":50395202519313,"sku":"CIN1627050116VG","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/B00RWSFKT4.jpg?v=1750763444"},{"product_id":"automated-grammatical-error-detection-for-language-learners-book-claudia-leacock-9781627050135","title":"Automated Grammatical Error Detection for Language Learners","description":"It has been estimated that over a billion people are using or learning English as a second or foreign language, and the numbers are growing not only for English but for other languages as well. 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Table of Contents: Representations and Linguistic Data \/ Decoding: Making Predictions \/ Learning Structure from Annotated Data \/ Learning Structure from Incomplete Data \/ Beyond Decoding: Inference","brand":"WoB","offers":[{"title":"- \/ - \/ -","offer_id":51061531410705,"sku":"","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ NEW \/ INGRAM","offer_id":51061534687505,"sku":"NIN9783031010156","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ NEW \/ INGRAM","offer_id":52427876368657,"sku":"NLS9783031010156","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/3031010159.jpg?v=1751157900"},{"product_id":"learning-to-rank-for-information-retrieval-and-natural-language-processing-secon-book-hang-li-9783031010279","title":"Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition","description":"Learning to rank refers to machine learning techniques for training a model in a ranking task. 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It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet \u0026amp; ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count, Markov Chain, and CRanking. 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Table of Contents: Learning to Rank \/ Learning for Ranking Creation \/ Learning for Ranking Aggregation \/ Methods of Learning to Rank \/ Applications of Learning to Rank \/ Theory of Learning to Rank \/ Ongoing and Future Work","brand":"WoB","offers":[{"title":"- \/ - \/ -","offer_id":51061556674833,"sku":"","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ NEW \/ INGRAM","offer_id":51061559394577,"sku":"NIN9783031010279","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ NEW \/ INGRAM","offer_id":52430747402513,"sku":"NLS9783031010279","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/3031010272.jpg?v=1751317382"},{"product_id":"natural-language-processing-for-historical-texts-book-michael-piotrowski-9783031010187","title":"Natural Language Processing for Historical Texts","description":"More and more historical texts are becoming available in digital form. 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The book starts with an overview of methods for the acquisition of historical texts (scanning and OCR), discusses text encoding and annotation schemes, and presents examples of corpora of historical texts in a variety of languages. The book then discusses specific methods, such as creating part-of-speech taggers for historical languages or handling spelling variation. A final chapter analyzes the relationship between NLP and the digital humanities. Certain recently emerging textual genres, such as SMS, social media, and chat messages, or newsgroup and forum postings share a number of properties with historical texts, for example, nonstandard orthography and grammar, and profuse use of abbreviations. The methods and techniques required for the effective processing of historical texts are thus also of interest for research in other domains. 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