Text Representation

This book brings together linguistics and psycholinguistics. Text representation is considered a cognitive entity: a mental construct that plays a crucial role in both text production and text understanding.The focus is on referential and relational coherence and the role of linguistic characteristics as processing instructions from a text linguistic and discourse psychology point of view. Consequently, this book presents various research methodologies: linguistic analysis, text analysis, corpus linguistics, computational linguistics, argumentation analysis, and the experimental psycholinguistic study of text processing. The authors compare, test, and evaluate linguistic and processing theories of text representation.A state of the art volume in an emerging field of interest, located at the very heart of our communicative behavior: the study of text and text representation.

This book brings together linguistics and psycholinguistics. Text representation is considered a cognitive entity: a mental construct that plays a crucial role in both text production and text understanding. The focus is on referential and relational coherence and the role of linguistic characteristics as processing instructions from a text linguistic and discourse psychology point of view. Consequently, this book presents various research methodologies: linguistic analysis, text analysis, corpus linguistics, computational linguistics, argumentation analysis, and the experimental psycholinguistic study of text processing. The authors compare, test, and evaluate linguistic and processing theories of text representation. A state of the art volume in an emerging field of interest, located at the very heart of our communicative behavior: the study of text and text representation.

Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail. With this book, you’ll: Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP Implement and evaluate different NLP applications using machine learning and deep learning methods Fine-tune your NLP solution based on your business problem and industry vertical Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages Produce software solutions following best practices around release, deployment, and DevOps for NLP systems Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Information representation and retrieval : an overview -- Information representation I : basic approaches -- Information representation II : other related topics -- Language in information representation and retrieval -- Retrieval techniques and query representation -- Retrieval approaches -- Information retrieval models -- Information retrieval systems -- Retrieval of information unique in content or format -- The user dimension in information representation and retrieval -- Evaluation of information representation and retrieval -- Artificial intelligence in information representation and retrieval.

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.

"Anarchism and the Crisis of Representation is intended to provide readers of literary criticism, art history, political philosophy, and the social sciences with a fresh perspective from which to revisit dead-end theoretical debates over concepts such as "agency," "essentialism," and "realism" - and, at the same time, to offer a new take on anarchism itself, challenging conventional readings of the tradition. The anarchism that emerges from this reinterpretation is neither a musty rationalism nor a millenarian irrationalism, but a living body of thought that points beyond the sterile antinomies of post-modern and Marxist theory."--BOOK JACKET.