This book explores the cognitive plausibility of computational language models and why it’s an important factor in their development and evaluation. The authors present the idea that more can be learned about cognitive plausibility of computational language models by linking signals of cognitive processing load in humans to interpretability methods that allow for exploration of the hidden mechanisms of neural models. The book identifies limitations when applying the existing methodology for representational analyses to contextualized settings and critiques the current emphasis on form over more grounded approaches to modeling language. The authors discuss how novel techniques for transfer and curriculum learning could lead to cognitively more plausible generalization capabilities in models. The book also highlights the importance of instance-level evaluation and includes thorough discussion of the ethical considerations that may arise throughout the various stages of cognitive plausibility research.
Human language acquisition has been studied for centuries, but using computational modeling for such studies is a relatively recent trend. However, computational approaches to language learning have become increasingly popular, mainly due to advances in developing machine learning techniques, and the availability of vast collections of experimental data on child language learning and child-adult interaction. Many of the existing computational models attempt to study the complex task of learning a language under cognitive plausibility criteria (such as memory and processing limitations that humans face), and to explain the developmental stages observed in children. By simulating the process of child language learning, computational models can show us which linguistic representations are learnable from the input that children have access to, and which mechanisms yield the same patterns of behaviour that children exhibit during this process. In doing so, computational modeling provides insight into the plausible mechanisms involved in human language acquisition, and inspires the development of better language models and techniques. This book provides an overview of the main research questions in the field of human language acquisition. It reviews the most commonly used computational frameworks, methodologies and resources for modeling child language learning, and the evaluation techniques used for assessing these computational models. The book is aimed at cognitive scientists who want to become familiar with the available computational methods for investigating problems related to human language acquisition, as well as computational linguists who are interested in applying their skills to the study of child language acquisition. Different aspects of language learning are discussed in separate chapters, including the acquisition of the individual words, the general regularities which govern word and sentence form, and the associations between form and meaning. For each of these aspects, the challenges of the task are discussed and the relevant empirical findings on children are summarized. Furthermore, the existing computational models that attempt to simulate the task under study are reviewed, and a number of case studies are presented. Table of Contents: Overview / Computational Models of Language Learning / Learning Words / Putting Words Together / Form--Meaning Associations / Final Thoughts
This comprehensive reference work provides an overview of the concepts, methodologies, and applications in computational linguistics and natural language processing (NLP). Features contributions by the top researchers in the field, reflecting the work that is driving the discipline forward Includes an introduction to the major theoretical issues in these fields, as well as the central engineering applications that the work has produced Presents the major developments in an accessible way, explaining the close connection between scientific understanding of the computational properties of natural language and the creation of effective language technologies Serves as an invaluable state-of-the-art reference source for computational linguists and software engineers developing NLP applications in industrial research and development labs of software companies
Questions related to language acquisition have been of interest for many centuries, as children seem to acquire a sophisticated capacity for processing language with apparent ease, in the face of ambiguity, noise and uncertainty. However, with recent advances in technology and cognitive-related research it is now possible to conduct large-scale computational investigations of these issues The book discusses some of the latest theoretical and practical developments in the areas involved, including computational models for language tasks, tools and resources that help to approximate the linguistic environment available to children during acquisition, and discussions of challenging aspects of language that children have to master. This is a much-needed collection that provides a cross-section of recent multidisciplinary research on the computational modeling of language acquisition. It is targeted at anyone interested in the relevance of computational techniques for understanding language acquisition. Readers of this book will be introduced to some of the latest approaches to these tasks including: * Models of acquisition of various types of linguistic information (from words to syntax and semantics) and their relevance to research on human language acquisition * Analysis of linguistic and contextual factors that influence acquisition * Resources and tools for investigating these tasks Each chapter is presented in a self-contained manner, providing a detailed description of the relevant aspects related to research on language acquisition, and includes illustrations and tables to complement these in-depth discussions. Though there are no formal prerequisites, some familiarity with the basic concepts of human and computational language acquisition is beneficial.
This study explores the design and application of natural language text-based processing systems, based on generative linguistics, empirical copus analysis, and artificial neural networks. It emphasizes the practical tools to accommodate the selected system.
The study of language has changed substantially in the last decades. In particular, the development of new technologies has allowed the emergence of new experimental techniques which complement more traditional approaches to data in linguistics (like informal reports of native speakers’ judgments, surveys, corpus studies, or fieldwork). This move is an enriching feature of contemporary linguistics, allowing for a better understanding of a phenomenon as complex as natural language, where all sorts of factors (internal and external to the individual) interact (Chomsky 2005). This has generated some sort of divergence not only in research approaches, but also in the phenomena studied, with an increasing specialization between subfields and accounts. At the same time, it has also led to subfield isolation and methodological a priori, with some researchers even claiming that theoretical linguistics has little to offer to cognitive science (see for instance Edelman & Christiansen 2003). We believe that this view of linguistics (and cognitive science as a whole) is misguided, and that the complementarity of different approaches to such a multidimensional phenomenon as language should be highlighted for convergence and further development of its scientific study (see also Jackendoff 1988, 2007; Phillips & Lasnik 2003; den Dikken, Bernstein, Tortora & Zanuttini 2007; Sprouse, Schütze & Almeida 2013; Phillips 2013).
The Routledge Handbook of Translation and Cognition provides a comprehensive, state-of-the-art overview of how translation and cognition relate to each other, discussing the most important issues in the fledgling sub-discipline of Cognitive Translation Studies (CTS), from foundational to applied aspects. With a strong focus on interdisciplinarity, the handbook surveys concepts and methods in neighbouring disciplines that are concerned with cognition and how they relate to translational activity from a cognitive perspective. Looking at different types of cognitive processes, this volume also ventures into emergent areas such as neuroscience, artificial intelligence, cognitive ergonomics and human–computer interaction. With an editors’ introduction and 30 chapters authored by leading scholars in the field of Cognitive Translation Studies, this handbook is the essential reference and resource for students and researchers of translation and cognition and will also be of interest to those working in bilingualism, second-language acquisition and related areas.
These three volumes (CCIS 442, 443, 444) constitute the proceedings of the 15th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2014, held in Montpellier, France, July 15-19, 2014. The 180 revised full papers presented together with five invited talks were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on uncertainty and imprecision on the web of data; decision support and uncertainty management in agri-environment; fuzzy implications; clustering; fuzzy measures and integrals; non-classical logics; data analysis; real-world applications; aggregation; probabilistic networks; recommendation systems and social networks; fuzzy systems; fuzzy logic in boolean framework; management of uncertainty in social networks; from different to same, from imitation to analogy; soft computing and sensory analysis; database systems; fuzzy set theory; measurement and sensory information; aggregation; formal methods for vagueness and uncertainty in a many-valued realm; graduality; preferences; uncertainty management in machine learning; philosophy and history of soft computing; soft computing and sensory analysis; similarity analysis; fuzzy logic, formal concept analysis and rough set; intelligent databases and information systems; theory of evidence; aggregation functions; big data - the role of fuzzy methods; imprecise probabilities: from foundations to applications; multinomial logistic regression on Markov chains for crop rotation modelling; intelligent measurement and control for nonlinear systems.
Parallel processing is not only a general topic of interest for computer scientists and researchers in artificial intelligence, but it is gaining more and more attention in the community of scientists studying natural language and its processing (computational linguists, AI researchers, psychologists). The growing need to integrate large divergent bodies of knowledge in natural language processing applications, or the belief that massively parallel systems are the only ones capable of handling the complexities and subtleties of natural language, are just two examples of the reasons for this increasing interest.
This book presents the complete collection of peer-reviewed presentations at the 1999 Cognitive Science Society meeting, including papers, poster abstracts, and descriptions of conference symposia. For students and researchers in all areas of cognitive science.
