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Automating Linguistics offers an in-depth study of the history of the mathematisation and automation of the sciences of language. In the wake of the first mathematisation of the 1930s, two waves followed: machine translation in the 1950s and the development of computational linguistics and natural language processing in the 1960s. These waves were pivotal given the work of large computerised corpora in the 1990s and the unprecedented technological development of computers and software.Early machine translation was devised as a war technology originating in the sciences of war, amidst the amalgamate of mathematics, physics, logics, neurosciences, acoustics, and emerging sciences such as cybernetics and information theory. Machine translation was intended to provide mass translations for strategic purposes during the Cold War. Linguistics, in turn, did not belong to the sciences of war, and played a minor role in the pioneering projects of machine translation.Comparing the two trends, the present book reveals how the sciences of language gradually integrated the technologies of computing and software, resulting in the second-wave mathematisation of the study of language, which may be called mathematisation-automation. The integration took on various shapes contingent upon cultural and linguistic traditions (USA, ex-USSR, Great Britain and France). By contrast, working with large corpora in the 1990s, though enabled by unprecedented development of computing and software, was primarily a continuation of traditional approaches in the sciences of language sciences, such as the study of spoken and written texts, lexicography, and statistical studies of vocabulary.
This volume comprises two invited talks and fifteen selected papers, chosen from over 200 submissions to the 15th International Conference on the History of Language Sciences (ICHoLS XV). Originally scheduled to be held in Milan in 2020, the conference was postponed and moved online due to the COVID-19 pandemic. Held from August 23-27, 2021, it connected scholars from 30 countries across various time zones. The volume is divided into three parts. The first part, devoted to General and Particular Issues in the History of Linguistics, recalls classical authors in relation to contemporary ones as well as newly established disciplines and subtle epistemological inquiries. The second part, Antiquity, mainly investigates the Sanskrit language and various descriptive and didactic studies, approached from both ancient and contemporary metalinguistic frameworks. The third part deals with Sixteenth to Twentieth Century Works, ranging from the Tamil language to American archives, and from experimental phonostylistics to the history of monosemy.
Solving linguistic problems not infrequently is reduced to carrying out tasks that are computationally complex and therefore requires automation. In such situations, the difference between having and not having computational tools to handle the tasks is not a matter of economy of time and effort, but may amount to the difference between finding and not finding a solution at all. This book is an introduction to machine-aided linguistic discovery, a novel research area, arguing for the fruitfulness of the computational approach by presenting a basic conceptual apparatus and several intelligent discovery programmes. One of the systems models the fundamental Saussurian notion of system, and thus, for the first time, almost a century after the introduction of this concept and structuralism in general, linguists are capable of adequately handling this recurring, computationally complex task. Another system models the problem of searching for Greenbergian language universals and is capable of stating its discoveries in an intelligible form, viz. a comprehensive English language text, thus constituting the first computer program to generate a whole scientific article. Yet another system detects potential inconsistencies in genetic language classifications. The programmes are applied with noteworthy results to substantial problems from diverse linguistic disciplines such as structural semantics, phonology, typology and historical linguistics.
Statutory warning: Language is a minefield. Words that firms and consumers use can be dealbreakers! Today, firms have many language-based decisions to make—from the brand name to the language of their annual reports to what they should or shouldn’t say on social media. Moreover, consumers leave a goldmine of information via their words expressing their likes, dislikes, perceptions and attitudes. What the firm communicates and what consumers say have an impact on consumer attitudes, satisfaction, loyalty, and ultimately, on a firm's sales, market share and profits. In this book, Abhishek Borah meticulously and marvellously showcases the influence of language on business. Through examples ranging from Toyota to Tesla and Metallica to Mahatma Gandhi, you will read about how to improvise on social media, how changing the use of simple pronouns like ‘we’ and ‘you’ can affect a firm’s bottom line, how to spot a fake review online and much more. So whether you are just inquisitive about the role of language in affecting consumer and company behaviour or a student wondering about the utility of language analysis in understanding them, Mine Your Language will teach you to use language to influence, engage and predict!
A human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems. One of the original goals of artificial intelligence research was to endow intelligent agents with human-level natural language capabilities. Recent AI research, however, has focused on applying statistical and machine learning approaches to big data rather than attempting to model what people do and how they do it. In this book, Marjorie McShane and Sergei Nirenburg return to the original goal of recreating human-level intelligence in a machine. They present a human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems that emphasizes meaning--the deep, context-sensitive meaning that a person derives from spoken or written language.
Two central ideas in the movement toward advanced automation systems are the office-of-the-future (or office automation system), and the factory of-the-future (or factory automation system). An office automation system is an integrated system with diversified office equipment, communication devices, intelligent terminals, intelligent copiers, etc., for providing information management and control in a dis tributed office environment. A factory automation system is also an inte grated system with programmable machine tools, robots, and other pro cess equipment such as new "peripherals," for providing manufacturing information management and control. Such advanced automation systems can be regarded as the response to the demand for greater variety, greater flexibility, customized designs, rapid response, and 'Just-in-time" delivery of office services or manufac tured goods. The economy of scope, which allows the production of a vari ety of similar products in random order, gradually replaces the economy of scale derived from overall volume of operations. In other words, we are gradually switching from the production of large volumes of standard products to systems for the production of a wide variety of similar products in small batches. This is the phenomenon of "demassification" of the marketplace, as described by Alvin Toffier in The Third Wave.
From hidden connections in big data to bots spreading fake news, journalism is increasingly computer-generated. An expert in computer science and media explains the present and future of a world in which news is created by algorithm. Amid the push for self-driving cars and the roboticization of industrial economies, automation has proven one of the biggest news stories of our time. Yet the wide-scale automation of the news itself has largely escaped attention. In this lively exposé of that rapidly shifting terrain, Nicholas Diakopoulos focuses on the people who tell the stories—increasingly with the help of computer algorithms that are fundamentally changing the creation, dissemination, and reception of the news. Diakopoulos reveals how machine learning and data mining have transformed investigative journalism. Newsbots converse with social media audiences, distributing stories and receiving feedback. Online media has become a platform for A/B testing of content, helping journalists to better understand what moves audiences. Algorithms can even draft certain kinds of stories. These techniques enable media organizations to take advantage of experiments and economies of scale, enhancing the sustainability of the fourth estate. But they also place pressure on editorial decision-making, because they allow journalists to produce more stories, sometimes better ones, but rarely both. Automating the News responds to hype and fears surrounding journalistic algorithms by exploring the human influence embedded in automation. Though the effects of automation are deep, Diakopoulos shows that journalists are at little risk of being displaced. With algorithms at their fingertips, they may work differently and tell different stories than they otherwise would, but their values remain the driving force behind the news. The human–algorithm hybrid thus emerges as the latest embodiment of an age-old tension between commercial imperatives and journalistic principles.
The attempt to spot deception through its correlates in human behavior has a long history. Until recently, these efforts have concentrated on identifying individual "cues" that might occur with deception. However, with the advent of computational means to analyze language and other human behavior, we now have the ability to determine whether there are consistent clusters of differences in behavior that might be associated with a false statement as opposed to a true one. While its focus is on verbal behavior, this book describes a range of behaviors—physiological, gestural as well as verbal—that have been proposed as indicators of deception. An overview of the primary psychological and cognitive theories that have been offered as explanations of deceptive behaviors gives context for the description of specific behaviors. The book also addresses the differences between data collected in a laboratory and "real-world" data with respect to the emotional and cognitive state of the liar. It discusses sources of real-world data and problematic issues in its collection and identifies the primary areas in which applied studies based on real-world data are critical, including police, security, border crossing, customs, and asylum interviews; congressional hearings; financial reporting; legal depositions; human resource evaluation; predatory communications that include Internet scams, identity theft, and fraud; and false product reviews. Having established the background, this book concentrates on computational analyses of deceptive verbal behavior that have enabled the field of deception studies to move from individual cues to overall differences in behavior. The computational work is organized around the features used for classification from -gram through syntax to predicate-argument and rhetorical structure. The book concludes with a set of open questions that the computational work has generated.