@book {16975,
	title = {Analyzing Social Media Networks with NodeXL},
	year = {2010},
	month = {2010/08/27/},
	publisher = {Morgan Kaufmann},
	organization = {Morgan Kaufmann},
	abstract = {"Analyzing Social Media Networks with NodeXL provides a much needed resource for the social media research community, as it describes network theory, provides compelling examples using data sources like Twitter and Flickr, and highlights how to use a free sophisticated tool for analysis. This is the perfect book for anyone trying to analyze the behavior of online social networks and beyond." ---Adam Perer, Research Scientist, IBM Research  "This book provides a basic introduction to social network analysis, followed by practical instruction and examples on gathering data from online sources, importing into Excel, and then analyzing the data through Excel. The book will be important for promoting research in the area for those in information science, sociology, cultural studies, virtual community, and e-commerce."---Caroline Haythornthwaite, PhD, Professor, Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign  Businesses, entrepreneurs, individuals, and government agencies alike are looking to social network analysis (SNA) tools for insight into trends, connections, and fluctuations in social media. Microsoft{\textquoteright}s NodeXL is a free, open-source SNA plug-in for use with Excel. It provides instant graphical representation of relationships of complex networked data. But it goes further than other SNA tools - NodeXL was developed by a multidisciplinary team of experts that bring together information studies, computer science, sociology, human-computer interaction, and over 20 years of visual analytic theory and information visualization into a simple tool anyone can use. This makes NodeXL of interest not only to end-users but also to researchers and students studying visual and network analytics and their application in the real world.},
	keywords = {Business \& Economics / Marketing / Research, Computers / Computer Science, Computers / Data Processing, Computers / Database Management / Data Mining, Computers / General, Computers / Information Theory, Computers / Interactive \& Multimedia, Computers / Social Aspects / General, Computers / Social Aspects / Human-Computer Interaction, Computers / User Interfaces, data mining, Data mining - Computer programs, Data mining/ Computer programs, Information Visualization, Information visualization - Computer programs, Information visualization/ Computer programs, NodeXL, online social networks, Social Science / General, Social Science / Reference, Social Science / Sociology / General, Technology \& Engineering / General, Technology \& Engineering / Social Aspects, Webometrics, Webometrics - Computer programs, Webometrics/ Computer programs},
	isbn = {9780123822291},
	author = {Hansen,Derek and Shneiderman, Ben and Smith,Marc A.}
}
@book {16719,
	title = {The Balancing Act: Combining Symbolic and Statistical Approaches to Language},
	year = {1996},
	month = {1996/12/06/},
	publisher = {MIT Press},
	organization = {MIT Press},
	abstract = {Symbolic and statistical approaches to language have historically been at odds{\textemdash}the former viewed as difficult to test and therefore perhaps impossible to define, and the latter as descriptive but possibly inadequate. At the heart of the debate are fundamental questions concerning the nature of language, the role of data in building a model or theory, and the impact of the competence-performance distinction on the field of computational linguistics. Currently, there is an increasing realization in both camps that the two approaches have something to offer in achieving common goals.The eight contributions in this book explore the inevitable "balancing act" that must take place when symbolic and statistical approaches are brought together{\textemdash}including basic choices about what knowledge will be represented symbolically and how it will be obtained, what assumptions underlie the statistical model, what principles motivate the symbolic model, and what the researcher gains by combining approaches.The topics covered include an examination of the relationship between traditional linguistics and statistical methods, qualitative and quantitative methods of speech translation, study and implementation of combined techniques for automatic extraction of terminology, comparative analysis of the contributions of linguistic cues to a statistical word grouping system, automatic construction of a symbolic parser via statistical techniques, combining linguistic with statistical methods in automatic speech understanding, exploring the nature of transformation-based learning, and a hybrid symbolic/statistical approach to recovering from parser failures.},
	keywords = {computational linguistics, Computational linguistics - Congresses, Computational linguistics/ Congresses, Computers / Intelligence (AI) \& Semantics, Foreign Language Study / General, Language Arts \& Disciplines / Linguistics / General, linguistics, Linguistics - Statistical methods - Congresses, Linguistics/ Statistical methods/ Congresses, Social Science / General},
	isbn = {9780262611220},
	author = {Klavans,Judith L. and Resnik, Philip}
}
