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ENGLISH

Online Hate Speech - Analysis, Detection, And Mitigation

by Animesh Mukherjee, Mithun Das e Punyajoy Saha

~~47,30€~~ 42,57€ 10% OFF

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 The book concludes with an overview of the mitigation techniques for hate speech, including blocking or suspension of the accounts (hard technique) and counterspeech (soft technique), and a discussion of the effects of these techniques on social media platforms.
ENGLISH

Multilingual Entity Linking

by Chen-Tse Tsai, Dan Roth e Shyam Upadhyay

~~47,30€~~ 42,57€ 10% OFF

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 This book focuses on Entity Discovery and Linking (EDL), which is the problem of identifying concepts and entities, disambiguating them, and grounding them to one or more knowledge bases (KBs). The book covers the core challenges that apply to all EDL problems, as well as the additional challenges associated with cross-lingual EDL problems.
ENGLISH

Online Hate Speech - Analysis, Detection, And Mitigation

by Animesh Mukherjee, Mithun Das e Punyajoy Saha

~~46,36€~~ 41,72€ 10% OFF

 The book concludes with an overview of the mitigation techniques for hate speech, including blocking or suspension of the accounts (hard technique) and counterspeech (soft technique), and a discussion of the effects of these techniques on social media platforms.
ENGLISH

Narrative And Generative Ai - A Computational Account

by Inderjeet Mani

~~47,30€~~ 42,57€ 10% OFF

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 background: white;">This second edition provides an overview of the principal problems, approaches, and challenges in modeling the narrative structure of stories. background: white;">ausality, and time, proposing new methods for representing embedded narratives, fictional entities, tempo, and audience response.
ENGLISH

Automatic Question Generation

by Michael Flor

~~87,86€~~ 79,07€ 10% OFF

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 The author presents a variety of AQG system architectures, including generating questions from syntactic analyses, semantic resources, neural architectures, ontologies and knowledge graphs, and large language models.
ENGLISH

Multilingual Entity Linking

by Chen-Tse Tsai, Dan Roth e Shyam Upadhyay

~~46,36€~~ 41,72€ 10% OFF

 This book focuses on Entity Discovery and Linking (EDL), which is the problem of identifying concepts and entities, disambiguating them, and grounding them to one or more knowledge bases (KBs). The book covers the core challenges that apply to all EDL problems, as well as the additional challenges associated with cross-lingual EDL problems.
ENGLISH

Emotion Detection In Natural Language Processing

by Federica Cavicchio

~~33,11€~~ 29,80€ 10% OFF

 This book provides a practical guide on annotating emotions in natural language data and showcases how these annotations can improve Natural Language Processing (NLP) and Natural Language Understanding (NLU) models and applications.
ENGLISH

Validity, Reliability, And Significance - Empirical Methods For Nlp And Data Science

by Michael Hagmann e Stefan Riezler

~~47,30€~~ 42,57€ 10% OFF

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 The book focuses on model-based empirical methods where data annotations and model predictions are treated as training data for interpretable probabilistic models from the well-understood families of generalized additive models (GAMs) and linear mixed effects models (LMEMs).
ENGLISH

Validity, Reliability, And Significance - Empirical Methods For Nlp And Data Science

by Michael Hagmann e Stefan Riezler

~~46,36€~~ 41,72€ 10% OFF

 The book focuses on model-based empirical methods where data annotations and model predictions are treated as training data for interpretable probabilistic models from the well-understood families of generalized additive models (GAMs) and linear mixed effects models (LMEMs).
ENGLISH

Automatic Language Identification In Texts

by Krister Linden, Marcos Zampieri, Timothy Baldwin e Tommi Jauhiainen

~~47,30€~~ 42,57€ 10% OFF

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 This book provides readers with a brief account of the history of Language Identification (LI) research and a survey of the features and methods most used in LI literature. The authors identify open issues and discuss the applications of LI and related tasks and proposes future directions for research in LI.
ENGLISH

Cognitive Plausibility In Natural Language Processing

by Lisa Beinborn e Nora Hollenstein

47,30€ + 10% CARD

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 This book explores the cognitive plausibility of computational language models and why itâ€™s an important factor in their development and evaluation.
ENGLISH

Building And Using Comparable Corpora For Multilingual Natural Language Processing

by Pierre Zweigenbaum, Reinhard Rapp e Serge Sharoff

47,30€ + 10% CARD

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 This book provides a comprehensive overview of methods to build comparable corpora and of their applications, including machine translation, cross-lingual transfer, and various kinds of multilingual natural language processing.
ENGLISH

Bayesian Analysis In Natural Language Processing

by Shay Cohen

119,24€ + 10% CARD

 Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze language. Since then, the use of statistical techniques in NLP has evolved in several ways. One such example of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP. This Bayesian approach to NLP has come to accommodate for various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples. We cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area. These methods and algorithms are partially borrowed from both machine learning and statistics and are partially developed "in-house" in NLP. We cover inference techniques such as Markov chain Monte Carlo sampling and variational inference, Bayesian estimation, and nonparametric modeling. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we cover some of the fundamental modeling techniques in NLP, such as grammar modeling and their use with Bayesian analysis.
ENGLISH

Natural Language Processing For Social Media

by Atefeh Farzindar

105,99€ + 10% CARD

 In recent years, online social networking has revolutionized interpersonal communication. The newer research on language analysis in social media has been increasingly focusing on the latter''s impact on our daily lives, both on a personal and a professional level. Natural language processing (NLP) is one of the most promising avenues for social media data processing. It is a scientific challenge to develop powerful methods and algorithms which extract relevant information from a large volume of data coming from multiple sources and languages in various formats or in free form. We discuss the challenges in analyzing social media texts in contrast with traditional documents. Research methods in information extraction, automatic categorization and clustering, automatic summarization and indexing, and statistical machine translation need to be adapted to a new kind of data. This book reviews the current research on Natural Language Processing (NLP) tools and methods for processing the non-traditional information from social media data that is available in large amounts (big data), and shows how innovative NLP approaches can integrate appropriate linguistic information in various fields such as social media monitoring, health care, business intelligence, industry, marketing, and security and defense. We review the existing evaluation metrics for NLP and social media applications, and the new efforts in evaluation campaigns or shared tasks on new datasets collected from social media. Such tasks are organized by the Association for Computational Linguistics (such as SemEval tasks) or by the National Institute of Standards and Technology via the Text REtrieval Conference (TREC) and the Text Analysis Conference (TAC). In the concluding chapter, we discuss the importance of this dynamic discipline and its great potential for NLP in the coming decade, in the context of changes in mobile technology, cloud computing, and social networking.
ENGLISH

Learning To Rank For Information Retrieval And Natural Language Processing

by Hang Li

105,99€ + 10% CARD

 Learning to rank refers to machine learning techniques for training the 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 the problem recently and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, existing approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings.Ranking creation (or ranking) is the major problem in learning to rank. 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 SVM, Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include PRank, OC SVM, Ranking SVM, IR SVM, GBRank, RankNet, LambdaRank, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, 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: Introduction / 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
ENGLISH

Automated Grammatical Error Detection For Language Learners

by Claudia Leacock, Joel Tetreault, Martin Chodorow e Michael Gamon

105,99€ + 10% CARD

 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. These language learners provide a burgeoning market for tools that help identify and correct learners'' writing errors. Unfortunately, the errors targeted by typical commercial proofreading tools do not include those aspects of a second language that are hardest to learn. This volume describes the types of constructions English language learners find most difficult -- constructions containing prepositions, articles, and collocations. It provides an overview of the automated approaches that have been developed to identify and correct these and other classes of learner errors in a number of languages. Error annotation and system evaluation are particularly important topics in grammatical error detection because there are no commonly accepted standards. Chapters in the book describe the options available to researchers, recommend best practices for reporting results, and present annotation and evaluation schemes. The final chapters explore recent innovative work that opens new directions for research. It is the authors'' hope that this volume will contribute to the growing interest in grammatical error detection by encouraging researchers to take a closer look at the field and its many challenging problems. Table of Contents: Introduction / History of Automated Grammatical Error Detection / Special Problems of Language Learners / Language Learner Data / Evaluating Error Detection Systems / Article and Preposition Errors / Collocation Errors / Different Approaches for Different Errors / Annotating Learner Errors / New Directions / Conclusion
ENGLISH

Lifelong And Continual Learning Dialogue Systems

by Bing Liu e Sahisnu Mazumder

~~46,36€~~ 41,72€ 10% OFF

 This book introduces the new paradigm of lifelong and continual learning dialogue systems to endow dialogue systems with the ability to learn continually by themselves through their own self-initiated interactions with their users and the working environments.
ENGLISH

Automatic Language Identification In Texts

by Krister Linden, Marcos Zampieri, Timothy Baldwin e Tommi Jauhiainen

~~46,36€~~ 41,72€ 10% OFF

 This book provides readers with a brief account of the history of Language Identification (LI) research and a survey of the features and methods most used in LI literature. The authors identify open issues and discuss the applications of LI and related tasks and proposes future directions for research in LI.
ENGLISH

Lifelong And Continual Learning Dialogue Systems

by Bing Liu e Sahisnu Mazumder

~~47,30€~~ 42,57€ 10% OFF

free shipping

 This book introduces the new paradigm of lifelong and continual learning dialogue systems to endow dialogue systems with the ability to learn continually by themselves through their own self-initiated interactions with their users and the working environments.
ENGLISH

Cognitive Plausibility In Natural Language Processing

by Lisa Beinborn e Nora Hollenstein

46,36€ + 10% CARD

 This book explores the cognitive plausibility of computational language models and why itâ€™s an important factor in their development and evaluation.
ENGLISH

Building And Using Comparable Corpora For Multilingual Natural Language Processing

by Pierre Zweigenbaum, Reinhard Rapp e Serge Sharoff

46,36€ + 10% CARD

 This book provides a comprehensive overview of methods to build comparable corpora and of their applications, including machine translation, cross-lingual transfer, and various kinds of multilingual natural language processing.
ENGLISH

Semantic Relations Between Nominals

by Diarmuid O Seaghdha, Preslav Nakov, Stan Szpakowicz e Vivi Nastase

35,76€ + 10% CARD

 People make sense of a text by identifying the semantic relations which connect the entities or concepts described by that text. A system which aspires to human-like performance must also be equipped to identify, and learn from, semantic relations in the texts it processes. Understanding even a simple sentence such as "Opportunity and Curiosity find similar rocks on Mars" requires recognizing relations (rocks are located on Mars, signalled by the word on) and drawing on already known relations (Opportunity and Curiosity are instances of the class of Mars rovers). A language-understanding system should be able to find such relations in documents and progressively build a knowledge base or even an ontology. Resources of this kind assist continuous learning and other advanced language-processing tasks such as text summarization, question answering and machine translation. The book discusses the recognition in text of semantic relations which capture interactions between base noun phrases.After a brief historical background, we introduce a range of relation inventories of varying granularity, which have been proposed by computational linguists. There is also variation in the scale at which systems operate, from snippets all the way to the whole Web, and in the techniques of recognizing relations in texts, from full supervision through weak or distant supervision to self-supervised or completely unsupervised methods. A discussion of supervised learning covers available datasets, feature sets which describe relation instances, and successful algorithms. An overview of weakly supervised and unsupervised learning zooms in on the acquisition of relations from large corpora with hardly any annotated data. We show how bootstrapping from seed examples or patterns scales up to very large text collections on the Web. We also present machine learning techniques in which data redundancy and variability lead to fast and reliable relation extraction.
ENGLISH

Natural Language Processing For Social Media, Second Edition

by Atefeh Farzindar e Diana Inkpen

58,29€ + 10% CARD

 In recent years, online social networking has revolutionized interpersonal communication. The newer research on language analysis in social media has been increasingly focusing on the latter''s impact on our daily lives, both on a personal and a professional level. Natural language processing (NLP) is one of the most promising avenues for social media data processing. It is a scientific challenge to develop powerful methods and algorithms which extract relevant information from a large volume of data coming from multiple sources and languages in various formats or in free form. We discuss the challenges in analyzing social media texts in contrast with traditional documents.Research methods in information extraction, automatic categorization and clustering, automatic summarization and indexing, and statistical machine translation need to be adapted to a new kind of data. This book reviews the current research on NLP tools and methods for processing the non-traditional information from social media data that is available in large amounts (big data), and shows how innovative NLP approaches can integrate appropriate linguistic information in various fields such as social media monitoring, healthcare, business intelligence, industry, marketing, and security and defence.We review the existing evaluation metrics for NLP and social media applications, and the new efforts in evaluation campaigns or shared tasks on new datasets collected from social media. Such tasks are organized by the Association for Computational Linguistics (such as SemEval tasks) or by the National Institute of Standards and Technology via the Text REtrieval Conference (TREC) and the Text Analysis Conference (TAC). In the concluding chapter, we discuss the importance of this dynamic discipline and its great potential for NLP in the coming decade, in the context of changes in mobile technology, cloud computing, virtual reality, and social networking.In this second edition, we have added information about recent progress in the tasks and applications presented in the first edition. We discuss new methods and their results. The number of research projects and publications that use social media data is constantly increasing due to continuously growing amounts of social media data and the need to automatically process them. We have added 85 new references to the more than 300 references from the first edition. Besides updating each section, we have added a new application (digital marketing) to the section on media monitoring and we have augmented the section on healthcare applications with an extended discussion of recent research on detecting signs of mental illness from social media.
ENGLISH

Statistical Methods For Annotation Analysis

by Massimo Poesio, Ron Artstein e Silviu Paun

74,34€ + 10% CARD

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 The focus of this book is primarily on Natural Language Processing, the area of AI devoted to the development of models of language interpretation and production, but many if not most of the methods discussed here are also applicable to other areas of AI, or indeed, to other areas of Data Science.
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