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The literary imagination may take flight on the wings of metaphor, but hard-headed scientists are just as likely as doe-eyed poets to reach for a metaphor when the descriptive need arises. Metaphor is a pervasive aspect of every genre of text and every register of speech, and is as useful for describing the inner workings of a "black hole" (itself a metaphor) as it is the affairs of the human heart. The ubiquity of metaphor in natural language thus poses a significant challenge for Natural Language Processing (NLP) systems and their builders, who cannot afford to wait until the problems of literal language have been solved before turning their attention to figurative phenomena. This book off...
This book discusses the state of the art of automated essay scoring, its challenges and its potential. One of the earliest applications of artificial intelligence to language data (along with machine translation and speech recognition), automated essay scoring has evolved to become both a revenue-generating industry and a vast field of research, with many subfields and connections to other NLP tasks. In this book, we review the developments in this field against the backdrop of Elias Page's seminal 1966 paper titled "The Imminence of Grading Essays by Computer." Part 1 establishes what automated essay scoring is about, why it exists, where the technology stands, and what are some of the main...
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 o...
Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Suc...
This book constitutes the thoroughly refereed post-conference proceedings of the workshop on Usable Security, USEC 2012, and the third Workshop on Ethics in Computer Security Research, WECSR 2012, held in conjunction with the 16th International Conference on Financial Cryptology and Data Security, FC 2012, in Kralendijk, Bonaire. The 13 revised full papers presented were carefully selected from numerous submissions and cover all aspects of data security. The goal of the USEC workshop was to engage on all aspects of human factors and usability in the context of security. The goal of the WECSR workshop was to continue searching for a new path in computer security that is Institutional review boards at academic institutions, as well as compatible with ethical guidelines for societies at government institutions.
This book presents a taxonomy framework and survey of methods relevant to explaining the decisions and analyzing the inner workings of Natural Language Processing (NLP) models. The book is intended to provide a snapshot of Explainable NLP, though the field continues to rapidly grow. The book is intended to be both readable by first-year M.Sc. students and interesting to an expert audience. The book opens by motivating a focus on providing a consistent taxonomy, pointing out inconsistencies and redundancies in previous taxonomies. It goes on to present (i) a taxonomy or framework for thinking about how approaches to explainable NLP relate to one another; (ii) brief surveys of each of the classes in the taxonomy, with a focus on methods that are relevant for NLP; and (iii) a discussion of the inherent limitations of some classes of methods, as well as how to best evaluate them. Finally, the book closes by providing a list of resources for further research on explainability.
This book constitutes the proceedings of the 20th International Conference on Theory and Practice of Digital Libraries, TPDL 2016, held in Hannover, Germany, in September 2016. The 28 full papers, 5 posters and 8 short papers presented in this volume were carefully reviewed and selected from 93 submissions. They were organized in topical sections named: Digital Library Design; User Aspects; Search; Web Archives; Semantics; Multimedia and Time Aspects; Digital Library Evaluation; Digital Humanities; e-Infrastructures.
This book presents the development of a classification scheme to organize and represent ransomware threat knowledge through the implementation of an innovative methodology centered around the semantic annotation of domain-specific source documentation. By combining principles from computer science, document management, and semantic data processing, the research establishes an innovative framework to organize ransomware data extracted from specialized source texts in a systematic classification system. Through detailed chapters, the book explores the process of applying semantic annotation to a specialized corpus comprising CVE prose descriptions linked to known ransomware threats. This appro...
Dan Everett is a renowned linguist with an unparalleled breadth of contributions, ranging from fieldwork to linguistic theory, including phonology, morphology, syntax, semantics, sociolinguistics, psycholinguistics, historical linguistics, philosophy of language, and philosophy of linguistics. Born on the U.S. Mexican border, Daniel Everett faced much adversity growing up and was sent as a missionary to convert the Pirahã in the Amazonian jungle, a group of people who speak a language that no outsider had been able to become proficient in. Although no Pirahã person was successfully converted, Everett successfully learned and studied Pirahã, as well as multiple other languages in the Ameri...
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.