Linguistic Fundamentals for Natural Language Processing II: 100 Essentials from Semantics and Pragmatics, Emily M. Bender, Alex Lascarides

Автор: Bender Emily M., Lascarides Alex
Название: Linguistic Fundamentals for Natural Language Processing II: 100 Essentials from Semantics and Pragmatics
ISBN: 1681736217 ISBN-13(EAN): 9781681736211
Издательство: Mare Nostrum (Eurospan)
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Цена: 104410.00 T
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Описание: Meaning is a fundamental concept in Natural Language Processing (NLP), in the tasks of both Natural Language Understanding (NLU) and Natural Language Generation (NLG).

This is because the aims of these fields are to build systems that understand what people mean when they speak or write, and that can produce linguistic strings that successfully express to people the intended content. In order for NLP to scale beyond partial, task-specific solutions, researchers in these fields must be informed by what is known about how humans use language to express and understand communicative intents. The purpose of this book is to present a selection of useful information about semantics and pragmatics, as understood in linguistics, in a way that's accessible to and useful for NLP practitioners with minimal (or even no) prior training in linguistics.

Автор: Emily M. Bender
Название: Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and Syntax
ISBN: 1627050116 ISBN-13(EAN): 9781627050111
Издательство: Mare Nostrum (Eurospan)
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Цена: 46200.00 T
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Описание: Presents in a succinct and accessible fashion information about the morphological and syntactic structure of human languages that can be useful in creating more linguistically sophisticated, more language-independent, and thus more successful NLP systems.

Автор: Luis Mart?nez; Rosa M. Rodriguez; Francisco Herrer
Название: The 2-tuple Linguistic Model
ISBN: 3319796658 ISBN-13(EAN): 9783319796659
Издательство: Springer
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Цена: 46570.00 T
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Автор: Henk Zeevat; Hans-Christian Schmitz
Название: Bayesian Natural Language Semantics and Pragmatics
ISBN: 3319170635 ISBN-13(EAN): 9783319170633
Издательство: Springer
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Цена: 102480.00 T
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Описание: The contributions in this volume focus on the Bayesian interpretation of natural languages, which is widely used in areas of artificial intelligence, cognitive science, and computational linguistics.

Автор: Vladimir Fomichov A.
Название: Semantics-Oriented Natural Language Processing
ISBN: 1489982809 ISBN-13(EAN): 9781489982803
Издательство: Springer
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Цена: 83810.00 T
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Описание: Exploring key issues in developing semantics-oriented natural language processing systems, this book describes structured meanings of NL-texts by defining a new class of formal languages called standard knowledge languages (SK languages) using systems theory.

Автор: Henk Zeevat; Hans-Christian Schmitz
Название: Bayesian Natural Language Semantics and Pragmatics
ISBN: 3319386255 ISBN-13(EAN): 9783319386256
Издательство: Springer
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Цена: 93160.00 T
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Описание: The contributions in this volume focus on the Bayesian interpretation of natural languages, which is widely used in areas of artificial intelligence, cognitive science, and computational linguistics.

Автор: Goldberg Yoav
Название: Neural Network Methods in Natural Language Processing
ISBN: 1627052984 ISBN-13(EAN): 9781627052986
Издательство: Mare Nostrum (Eurospan)
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Цена: 76690.00 T
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Описание: 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 (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries.The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.

Автор: Manfred Stede, Jodi Schneider
Название: Argumentation Mining
ISBN: 1681734613 ISBN-13(EAN): 9781681734613
Издательство: Mare Nostrum (Eurospan)
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Цена: 97950.00 T
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Описание: Argumentation mining is an application of natural language processing (NLP) that emerged a few years ago and has recently enjoyed considerable popularity, as demonstrated by a series of international workshops and by a rising number of publications at the major conferences and journals of the field. Its goals are to identify argumentation in text or dialogue; to construct representations of the constellation of claims, supporting and attacking moves (in different levels of detail); and to characterize the patterns of reasoning that appear to license the argumentation. Furthermore, recent work also addresses the difficult tasks of evaluating the persuasiveness and quality of arguments. Some of the linguistic genres that are being studied include legal text, student essays, political discourse and debate, newspaper editorials, scientific writing, and others.The book starts with a discussion of the linguistic perspective, characteristics of argumentative language, and their relationship to certain other notions such as subjectivity.Besides the connection to linguistics, argumentation has for a long time been a topic in Artificial Intelligence, where the focus is on devising adequate representations and reasoning formalisms that capture the properties of argumentative exchange. It is generally very difficult to connect the two realms of reasoning and text analysis, but we are convinced that it should be attempted in the long term, and therefore we also touch upon some fundamentals of reasoning approaches.Then the book turns to its focus, the computational side of mining argumentation in text. We first introduce a number of annotated corpora that have been used in the research. From the NLP perspective, argumentation mining shares subtasks with research fields such as subjectivity and sentiment analysis, semantic relation extraction, and discourse parsing. Therefore, many technical approaches are being borrowed from those (and other) fields. We break argumentation mining into a series of subtasks, starting with the preparatory steps of classifying text as argumentative (or not) and segmenting it into elementary units. Then, central steps are the automatic identification of claims, and finding statements that support or oppose the claim. For certain applications, it is also of interest to compute a full structure of an argumentative constellation of statements. Next, we discuss a few steps that try to 'dig deeper': to infer the underlying reasoning pattern for a textual argument, to reconstruct unstated premises (so-called 'enthymemes'), and to evaluate the quality of the argumentation. We also take a brief look at 'the other side' of mining, i.e., the generation or synthesis of argumentative text.The book finishes with a summary of the argumentation mining tasks, a sketch of potential applications, and a-necessarily subjective-outlook for the field.

Автор: Philip Williams, Michael Gertz, Matt Post, Philipp Koehn
Название: Syntax-based Statistical Machine Translation
ISBN: 1627059008 ISBN-13(EAN): 9781627059008
Издательство: Mare Nostrum (Eurospan)
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Цена: 72070.00 T
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Описание: Provides a comprehensive introduction to the most popular syntax-based statistical machine translation models, filling a gap in the current literature for researchers and developers in human language technologies. The heart of this book is a detailed introduction to decoding for syntax-based models.

Автор: Diana Maynard, Kalina Bontcheva
Название: Natural Language Processing for the Semantic Web
ISBN: 1627059091 ISBN-13(EAN): 9781627059091
Издательство: Mare Nostrum (Eurospan)
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Цена: 76690.00 T
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Описание: Introduces core natural language processing (NLP) technologies to non-experts in an easily accessible way, as a series of building blocks that lead the user to understand key technologies, why they are required, and how to integrate them into Semantic Web applications.

Название: Natural Language Processing: Concepts, Methodologies, Tools, and Applications
ISBN: 179980951X ISBN-13(EAN): 9781799809517
Издательство: Mare Nostrum (Eurospan)
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Цена: 2263800.00 T
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Описание: As technology continues to become more sophisticated, a computer's ability to understand, interpret, and manipulate natural language is also accelerating. Persistent research in the field of natural language processing enables an understanding of the world around us, in addition to opportunities for manmade computing to mirror natural language processes that have existed for centuries.

Natural Language Processing: Concepts, Methodologies, Tools, and Applications is a vital reference source on the latest concepts, processes, and techniques for communication between computers and humans. Highlighting a range of topics such as machine learning, computational linguistics, and semantic analysis, this multi-volume book is ideally designed for computer engineers, computer and software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of natural language processing.

Автор: Lucia Specia, Carolina Scarton, Gustavo Henrique Paetzold
Название: Quality Estimation for Machine Translation
ISBN: 1681733757 ISBN-13(EAN): 9781681733753
Издательство: Mare Nostrum (Eurospan)
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Цена: 87780.00 T
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Описание: Many applications within natural language processing involve performing text-to-text transformations, i.e., given a text in natural language as input, systems are required to produce a version of this text (e.g., a translation), also in natural language, as output. Automatically evaluating the output of such systems is an important component in developing text-to-text applications. Two approaches have been proposed for this problem: (i) to compare the system outputs against one or more reference outputs using string matching-based evaluation metrics and (ii) to build models based on human feedback to predict the quality of system outputs without reference texts. Despite their popularity, reference-based evaluation metrics are faced with the challenge that multiple good (and bad) quality outputs can be produced by text-to-text approaches for the same input. This variation is very hard to capture, even with multiple reference texts. In addition, reference-based metrics cannot be used in production (e.g., online machine translation systems), when systems are expected to produce outputs for any unseen input. In this book, we focus on the second set of metrics, so-called Quality Estimation (QE) metrics, where the goal is to provide an estimate on how good or reliable the texts produced by an application are without access to gold-standard outputs. QE enables different types of evaluation that can target different types of users and applications. Machine learning techniques are used to build QE models with various types of quality labels and explicit features or learnt representations, which can then predict the quality of unseen system outputs. This book describes the topic of QE for text-to-text applications, covering quality labels, features, algorithms, evaluation, uses, and state-of-the-art approaches. It focuses on machine translation as application, since this represents most of the QE work done to date. It also briefly describes QE for several other applications, including text simplification, text summarization, grammatical error correction, and natural language generation.