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Touch in the Time of Corona
A chronicle, a memoir, a reflection on the pandemic, and a cultural analysis of the new spatial, social, and epistemological forms that have arisen with it, this volume weaves together cultural history, aesthetics, and urban and digital studies. It looks at the particular ways in which the possibilities for touch, touching and being touched, both physically and affectively, are reconfigured by the pandemic. How are love, care, and humanity’s complex relationships with technology and nature played out in the interval between abandoned city centres and digitally mediated gatherings? How can we comprehend the reconfiguration of relationships through the human response to the pandemic as an experience that concerns us all but affects each of us in different ways? How do we think through the technological and material dependencies that the pandemic situation establishes? And how does this allow us to imagine the world beyond the pandemic—both utopian and dystopian? The essays in this book explore the new forms of intimacy and distance that are developing in the wake of COVID-19, offering a distinctive, topical analysis in the fields of urban and digital studies.
Embeddings in Natural Language Processing
Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP). Encoding information into a low-dimensional vector representation, which is easily integrable in modern machine learning models, has played a central role in the development of NLP. Embedding techniques initially focused on words, but the attention soon started to shift to other forms: from graph structures, such as knowledge bases, to other types of textual content, such as sentences and documents. This book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings. The book also provides an overview of recent developments in contextualized representations (e.g., ELMo and BERT) and explains their potential in NLP. Throughout the book, the reader can find both essential information for understanding a certain topic from scratch and a broad overview of the most successful techniques developed in the literature.
Algebraic Structures in Natural Language
Algebraic Structures in Natural Language addresses a central problem in cognitive science concerning the learning procedures through which humans acquire and represent natural language. Until recently algebraic systems have dominated the study of natural language in formal and computational linguistics, AI, and the psychology of language, with linguistic knowledge seen as encoded in formal grammars, model theories, proof theories and other rule-driven devices. Recent work on deep learning has produced an increasingly powerful set of general learning mechanisms which do not apply rule-based algebraic models of representation. The success of deep learning in NLP has led some researchers to que...
Information Retrieval Technology
This book constitutes the refereed proceedings of the Third Asia Information Retrieval Symposium, AIRS 2006. The book presents 34 revised full papers and 24 revised poster papers. All current issues in information retrieval are addressed: applications, systems, technologies and theoretical aspects of information retrieval in text, audio, image, video and multi-media data. The papers are organized in topical sections on text retrieval, search and extraction, text classification and indexing, and more.
Natural Language Processing – IJCNLP 2004
This book constitutes the thoroughly refereed post-proceedings of the First International Joint Conference on Natural Language Processing, IJCNLP 2004, held in Hainan Island, China in March 2004. The 84 revised full papers presented in this volume were carefully selected during two rounds of reviewing and improvement from 211 papers submitted. The papers are organized in topical sections on dialogue and discourse; FSA and parsing algorithms; information extractions and question answering; information retrieval; lexical semantics, ontologies, and linguistic resources; machine translation and multilinguality; NLP software and applications, semantic disambiguities; statistical models and machine learning; taggers, chunkers, and shallow parsers; text and sentence generation; text mining; theories and formalisms for morphology, syntax, and semantics; word segmentation; NLP in mobile information retrieval and user interfaces; and text mining in bioinformatics.
Conversational AI
This book provides a comprehensive introduction to Conversational AI. While the idea of interacting with a computer using voice or text goes back a long way, it is only in recent years that this idea has become a reality with the emergence of digital personal assistants, smart speakers, and chatbots. Advances in AI, particularly in deep learning, along with the availability of massive computing power and vast amounts of data, have led to a new generation of dialogue systems and conversational interfaces. Current research in Conversational AI focuses mainly on the application of machine learning and statistical data-driven approaches to the development of dialogue systems. However, it is impo...
From People to Entities: New Semantic Search Paradigms for the Web
The exponential growth of digital information available in companies and on the Web creates the need for search tools that can respond to the most sophisticated information needs. Many user tasks would be simplified if Search Engines would support typed search, and return entities instead of just Web documents. For example, an executive who tries to solve a problem needs to find people in the company who are knowledgeable about a certain topic._x000D_ In the first part of the book, we propose a model for expert finding based on the well-consolidated vector space model for Information Retrieval and investigate its effectiveness. In the second part of the book, we investigate different methods...
Large Language Models
Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs -- their intricate architecture, underlying algorithms, and ethical considerations -- require thorough exploration, creating a need for a comprehensive book on this subject. This book provides an authoritative exploration...
The Oxford Handbook of Computational Linguistics
Ruslan Mitkov's highly successful Oxford Handbook of Computational Linguistics has been substantially revised and expanded in this second edition. Alongside updated accounts of the topics covered in the first edition, it includes 17 new chapters on subjects such as semantic role-labelling, text-to-speech synthesis, translation technology, opinion mining and sentiment analysis, and the application of Natural Language Processing in educational and biomedical contexts, among many others. The volume is divided into four parts that examine, respectively: the linguistic fundamentals of computational linguistics; the methods and resources used, such as statistical modelling, machine learning, and corpus annotation; key language processing tasks including text segmentation, anaphora resolution, and speech recognition; and the major applications of Natural Language Processing, from machine translation to author profiling. The book will be an essential reference for researchers and students in computational linguistics and Natural Language Processing, as well as those working in related industries.
