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Social, Cultural, and Behavioral Modeling
This book constitutes the refereed proceedings of the 9th International Conference on Social, Cultural, and Behavioral Modeling & Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2016, held in Washington, DC, USA, in June/July 2016. The 38 full papers presented were carefully reviewed and selected from 78 submissions. The goal of this conference was to build a new community of social cyber scholars by bringing together and fostering interaction between members of the scientific, corporate, government and military communities interested in understanding, forecasting and impacting human socio-cultural behavior. For this three challenges have to be met: deep understanding, socio-cognitive reasoning, and re-usable computational technology. Thus papers come from a wide number of disciplines: computer science, psychology, sociology, communication science, public health, bioinformatics, political science, and organizational science.
Discourse Processing
Discourse Processing here is framed as marking up a text with structural descriptions on several levels, which can serve to support many language-processing or text-mining tasks. We first explore some ways of assigning structure on the document level: the logical document structure as determined by the layout of the text, its genre-specific content structure, and its breakdown into topical segments. Then the focus moves to phenomena of local coherence. We introduce the problem of coreference and look at methods for building chains of coreferring entities in the text. Next, the notion of coherence relation is introduced as the second important factor of local coherence. We study the role of c...
Empirical Approaches to Linguistic Theory
The mental representation of language cannot be directly observed but must be inferred and modelled from its effects at second hand. Linguists have traditionally responded to this in two ways, either going for a fairly data-light approach and valuing theoretical creativity, or pursuing just those goals for which data is available and trusting to data-driven descriptive work. More recently, advances in technology and experimental techniques have made data gathering easier and more accessible, so that a theoretically informed but empirically based approach is rapidly growing in popularity. This synthesis permits linguists to combine the intellectual hypothesis generation of the theoreticians with the ability to deliver hard answers of the empiricist. This volume is a collection of papers in this direction, using mostly experiment methods to yield insights into syntactic and semantic structures, language processing, and acquisition. Papers report corpus data, neurological investigations, child language studies, and fieldwork from minority languages.
Empirical Methods in Natural Language Generation
Natural language generation (NLG) is a subfield of natural language processing (NLP) that is often characterized as the study of automatically converting non-linguistic representations (e.g., from databases or other knowledge sources) into coherent natural language text. In recent years the field has evolved substantially. Perhaps the most important new development is the current emphasis on data-oriented methods and empirical evaluation. Progress in related areas such as machine translation, dialogue system design and automatic text summarization and the resulting awareness of the importance of language generation, the increasing availability of suitable corpora in recent years, and the organization of shared tasks for NLG, where different teams of researchers develop and evaluate their algorithms on a shared, held out data set have had a considerable impact on the field, and this book offers the first comprehensive overview of recent empirically oriented NLG research.
Approaches to Language: Data, Theory, and Explanation
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...
Expertise and Skill Acquisition
The research on human expertise and complex skill acquisition that Wlliam G. Chase performed in the decade between publication of the classic chess studies he conducted with Herb Simon in 1973 and his untimely and tragic death has proven profoundly influential and enduring. Its impact spans disciplines that include Psychology, Computer Science, Education, Cognitive Neuroscience, Medicine, and Human Factors. It has contributed significantly to the emergence of Cognitive Engineering and has led to significant applications in the areas of training and instruction and knowledge-based "intelligent" computational systems. Its influence can be seen in current discussions of intelligence, heritability, intellectual potential, and achievement found in the contemporary popular press. The chapters in this volume document the enduring scientific contributions of William G. Chase to current knowledge and understanding of human expertise and skill acquisition and applications his work has supported. It will be of interest to those researching, studying, and working in the multiple fields that were greatly influenced by Chase's work.
Machine Learning and Knowledge Discovery in Databases
The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, and 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.
Social, Cultural, and Behavioral Modeling
This book constitutes the refereed proceedings of the 10th International Conference on Social, Cultural, and Behavioral Modeling & Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2017, held in Washington, DC, USA, in July 2017. The 16 full papers and 27 short papers presented were carefully reviewed and selected from 79 submissions. Owing to its strong multi-disciplinary heritage, the papers represent a large range of disciplines including computer science, psychology, sociology, communication science, public health, bioinformatics, political science, and organizational science and use numerous types of computational methods such as machine learning, language technology, social network analysis and visualization, agent-based simulation, and statistics. They are organized in the following topical sections: behavioral and social sciences; cyber and intelligence applications; information, systems, and network sciences; and methodology.
