Yannis Haralambous – författare

The era of ASCII characters on green screens is long gone. Today, industry leaders such as Apple, HP, IBM, Microsoft, and Oracle have adopted the Unicode Worldwide Character Standard, and acceptance among developers is rapidly increasing. Yet much software and many web sites still use a host of standards, including PostScript, TrueType, TeX/Omega, SVG, Fontlab, FontForge, Metafont, Panose, and OpenType. This new reference book is a comprehensive guide to using fonts and typography on the Web and across a variety of operating systems and application software. "Fonts & Encodings" shows you how to take full advantage of the incredible number of typographic options available. Advanced material covers everything from designing glyphs to developing software that creates and processes fonts. This book explains information on fonts and typography that software and web developers need to know to get typography and fonts to work properly.

This reference is a fascinating and complete guide to using fonts and typography on the Web and across a variety of operating systems and application software. Fonts & Encodings shows you how to take full advantage of the incredible number of typographic options available, with advanced material that covers everything from designing glyphs to developing software that creates and processes fonts.The era of ASCII characters on green screens is long gone, and industry leaders such as Apple, HP, IBM, Microsoft, and Oracle have adopted the Unicode Worldwide Character Standard. Yet, many software applications and web sites still use a host of standards, including PostScript, TrueType, TeX/Omega, SVG, Fontlab, FontForge, Metafont, Panose, and OpenType. This book explores each option in depth, and provides background behind the processes that comprise today''s "digital space for writing":

Part I introduces Unicode, with a brief history of codes and encodings including ASCII. Learn about the morass of the data that accompanies each Unicode character, and how Unicode deals with normalization, the bidirectional algorithm, and the handling of East Asian characters.Part II discusses font management, including installation, tools for activation/deactivation, and font choices for three different systems: Windows, the Mac OS, and the X Window System (Unix).Part III deals with the technical use of fonts in two specific cases: the TeX typesetting system (and its successor, W, which the author co-developed) and web pages.Part IV describes methods for classifying fonts: Vox, Alessandrini, and Panose-1, which is used by Windows and the CSS standard. Learn about existing tools for creating (or modifying) fonts, including FontLab and FontForge, and become familiar with OpenType properties and AAT fonts.Nowhere else will you find the valuable technical information on fonts and typography that software developers, web developers, and graphic artists need to know to get typography and fonts to work properly.

Natural Language Processing is the branch of Artificial Intelligence involving language, be it in spoken or written modality. Teaching Natural Language Processing (NLP) is difficult because of its inherent connections with other disciplines, such as Linguistics, Cognitive Science, Knowledge Representation, Machine Learning, Data Science, and its latest avatar: Deep Learning. Most introductory NLP books favor one of these disciplines at the expense of others. Based on a course on Natural Language Processing taught by the author at IMT Atlantique for over a decade, this textbook considers three points of view corresponding to three different disciplines, while granting equal importance to each of them. As such, the book provides a thorough introduction to the topic following three main threads: the fundamental notions of Linguistics, symbolic Artificial Intelligence methods (based on knowledge representation languages), and statistical methods (involving both legacy machine learning and deep learning tools). Complementary to this introductory text is teaching material, such as exercises and labs with hints and expected results. Complete solutions with Python code are provided for educators on the SpringerLink webpage of the book. This material can serve for classes given to undergraduate and graduate students, or for researchers, instructors, and professionals in computer science or linguistics who wish to acquire or improve their knowledge in the field. The book is suitable and warmly recommended for self-study.

Natural Language Processing is the branch of Artificial Intelligence involving language, be it in spoken or written modality. Teaching Natural Language Processing (NLP) is difficult because of its inherent connections with other disciplines, such as Linguistics, Cognitive Science, Knowledge Representation, Machine Learning, Data Science, and its latest avatar: Deep Learning. Most introductory NLP books favor one of these disciplines at the expense of others. Based on a course on Natural Language Processing taught by the author at IMT Atlantique for over a decade, this textbook considers three points of view corresponding to three different disciplines, while granting equal importance to each of them. As such, the book provides a thorough introduction to the topic following three main threads: the fundamental notions of Linguistics, symbolic Artificial Intelligence methods (based on knowledge representation languages), and statistical methods (involving both legacy machine learning and deep learning tools). Complementary to this introductory text is teaching material, such as exercises and labs with hints and expected results. Complete solutions with Python code are provided for educators on the SpringerLink webpage of the book. This material can serve for classes given to undergraduate and graduate students, or for researchers, instructors, and professionals in computer science or linguistics who wish to acquire or improve their knowledge in the field. The book is suitable and warmly recommended for self-study.

Natural Language Processing is the branch of Artificial Intelligence involving language, be it in spoken or written modality. Teaching Natural Language Processing (NLP) is difficult because of its inherent connections with other disciplines, such as Linguistics, Cognitive Science, Knowledge Representation, Machine Learning, Data Science, and its latest avatar: Deep Learning. Most introductory NLP books favor one of these disciplines at the expense of others. Based on a course on Natural Language Processing taught by the author at IMT Atlantique for over a decade, this textbook considers three points of view corresponding to three different disciplines, while granting equal importance to each of them. As such, the book provides a thorough introduction to the topic following three main threads: the fundamental notions of Linguistics, symbolic Artificial Intelligence methods (based on knowledge representation languages), and statistical methods (involving both legacy machine learning and deep learning tools). Complementary to this introductory text is teaching material, such as exercises and labs with hints and expected results. Complete solutions with Python code are provided for educators on the SpringerLink webpage of the book. This material can serve for classes given to undergraduate and graduate students, or for researchers, instructors, and professionals in computer science or linguistics who wish to acquire or improve their knowledge in the field. The book is suitable and warmly recommended for self-study.

This volume contains the papers that were accepted for presentation at the - ternationalConferenceonT X,XML, andDigitalTypography,jointlyheld with E the 25th Annual Meeting of the T X Users Group in Xanthi, Greece in the sum- E mer of2004.Theterm"DigitalTypography"refersto thepreparationofprinted matter using only electronic computers and electronic printing devices, such as laser-jetprinters.Thedocumentpreparationprocessinvolvesmainlytheuseofa digital typesetting system as well as data representation technologies. TXand E its o?spring are beyond doubt the most successful current digital typesetters, while XML is the standard for text-based data representation for both business and scienti?c activities. Allpapersappearinginthisvolumewerefullyrefereedbythemembersofthe program committee. The papers were carefully selected to re?ect the research work that is being done in the ?eld of digital typography using T X and/or its E o?spring. The problems for which comprehensivesolutions have been proposed include proper multilingual document preparation and XML document processing and generation.The proposed solutions deal not simply with typesetting issues, but also related issues in document preparation, such as the manipulation of c- plex bibliographic databases, and automatic conversion of text expressed in one grammaticalsystem to a more recent one (as for the Greek language,converting between monotonic Greek and polytonic Greek). The conference is being graciously hosted by the Democritus University of Thrace in Xanthi and by the Greek T X Friends. We wish to thank Basil K.