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Similar Languages, Varieties, and Dialects
Studying language variation requires comprehensive interdisciplinary knowledge and new computational tools. This essential reference introduces researchers and graduate students in computer science, linguistics, and NLP to the core topics in language variation and the computational methods applied to similar languages, varieties, and dialects.
Neural Machine Translation
Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.
Dependency Parsing
Dependency-based methods for syntactic parsing have become increasingly popular in natural language processing in recent years. This book gives a thorough introduction to the methods that are most widely used today. After an introduction to dependency grammar and dependency parsing, followed by a formal characterization of the dependency parsing problem, the book surveys the three major classes of parsing models that are in current use: transition-based, graph-based, and grammar-based models. It continues with a chapter on evaluation and one on the comparison of different methods, and it closes with a few words on current trends and future prospects of dependency parsing. The book presupposes a knowledge of basic concepts in linguistics and computer science, as well as some knowledge of parsing methods for constituency-based representations. Table of Contents: Introduction / Dependency Parsing / Transition-Based Parsing / Graph-Based Parsing / Grammar-Based Parsing / Evaluation / Comparison / Final Thoughts
Semantic Role Labeling
This book is aimed at providing an overview of several aspects of semantic role labeling. Chapter 1 begins with linguistic background on the definition of semantic roles and the controversies surrounding them. Chapter 2 describes how the theories have led to structured lexicons such as FrameNet, VerbNet and the PropBank Frame Files that in turn provide the basis for large scale semantic annotation of corpora. This data has facilitated the development of automatic semantic role labeling systems based on supervised machine learning techniques. Chapter 3 presents the general principles of applying both supervised and unsupervised machine learning to this task, with a description of the standard...
Multi-Modal Sentiment Analysis
The natural interaction ability between human and machine mainly involves human-machine dialogue ability, multi-modal sentiment analysis ability, human-machine cooperation ability, and so on. To enable intelligent computers to have multi-modal sentiment analysis ability, it is necessary to equip them with a strong multi-modal sentiment analysis ability during the process of human-computer interaction. This is one of the key technologies for efficient and intelligent human-computer interaction. This book focuses on the research and practical applications of multi-modal sentiment analysis for human-computer natural interaction, particularly in the areas of multi-modal information feature repre...
Language, Cognition, and Computational Models
This book uses recent computational models to explore issues related to language and cognition.
Dependency Linguistics
This volume offers the reader a unique possibility to obtain a concise introduction to dependency linguistics and to learn about the current state of the art in the field. It unites the revised and extended versions of the linguistically-oriented papers to the First International Conference on Dependency Linguistics held in Barcelona. The contributions range from the discussion of definitional challenges of dependency at different levels of the linguistic model, its role beyond the classical grammatical description, and its annotation in dependency treebanks to concrete analyses of various cross-linguistic phenomena of syntax in its interplay with phonetics, morphology, and semantics, including phenomena for which classical simple phrase-structure based models have proven to be unsatisfactory. The volume will be thus of interest to both experts and newcomers to the field of dependency linguistics and its computational applications.
The Language of Fashion
Roland Barthes was one of the most widely influential thinkers of the 20th Century and his immensely popular and readable writings have covered topics ranging from wrestling to photography. The semiotic power of fashion and clothing were of perennial interest to Barthes and The Language of Fashion - now available in the Bloomsbury Revelations series - collects some of his most important writings on these topics. Barthes' essays here range from the history of clothing to the cultural importance of Coco Chanel, from Hippy style in Morocco to the figure of the dandy, from colour in fashion to the power of jewellery. Barthes' acute analysis and constant questioning make this book an essential read for anyone seeking to understand the cultural power of fashion.
Spoken Language Understanding
Spoken language understanding (SLU) is an emerging field in between speech and language processing, investigating human/ machine and human/ human communication by leveraging technologies from signal processing, pattern recognition, machine learning and artificial intelligence. SLU systems are designed to extract the meaning from speech utterances and its applications are vast, from voice search in mobile devices to meeting summarization, attracting interest from both commercial and academic sectors. Both human/machine and human/human communications can benefit from the application of SLU, using differing tasks and approaches to better understand and utilize such communications. This book cov...
Quality Estimation for Machine Translation
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, ref...
