000			03518nam a2200373Ia 4500
000			04050nam a22004095i 4500
001			978-3-031-26809-0
003			DE-He213
005			20240319120858.0
007			cr nn 008mamaa
008			230731s2023 sz | s |||| 0|eng d
020			_a9783031268090 _9978-3-031-26809-0
082			_a6.35
100			_aSproat, Richard. _931389
245			_aSymbols _cby Richard Sproat. _h[electronic resource] :
250			_a1st ed. 2023.
260			_aCham _bSpringer Nature Switzerland _c2023
300			_aXIII, 235 p. 91 illus., 57 illus. in color. _bonline resource.
520			_aFor millennia humans have used visible marks to communicate information. Modern examples of conventional graphical symbols include written language, and non-linguistic symbol systems such as mathematical symbology or traffic signs. The latter kinds of symbols convey information without reference to language. This book presents the first systematic study of graphical symbol systems, including a history of graphical symbols from the Paleolithic onwards, a taxonomy of non-linguistic systems - systems that are not tied to spoken language - and a survey of more than 25 such systems. One important feature of many non-linguistic systems is that, as in written language, symbols may be combined into complex "messages" if the information the system represents is itself complex. To illustrate, the author presents an in-depth comparison of two systems that had very similar functions, but very different structure: European heraldry and Japanese kamon. Writing first appeared in Mesopotamia about 5,000 years ago and is believed to have evolved from a previous non-linguistic accounting system. The exact mechanism is unknown, but crucial was the discovery that symbols can represent the sounds of words, not just the meanings. The book presents a novel neurologically-inspired hypothesis that writing evolved in an institutional context in which symbols were "dictated", thus driving an association between symbol and sound, and provides a computational simulation to support this hypothesis. The author further discusses some common fallacies about writing and non-linguistic systems, and how these relate to widely cited claims about statistical "evidence" for one or another system being writing. The book ends with some thoughts about the future of graphical symbol systems. The intended audience includes students, researchers, lecturers, professionals and scientists from fields like Natural Language Processing, Machine Learning, Archaeology and Semiotics, as well as general readers interested in language and/or writing systems and symbol systems. Richard Sproat is a Research Scientist at Google working on Deep Learning. He has a long-standing interest in writing systems and other graphical symbol systems.
650			_aComputational linguistics. _931390
650			_aComputational Linguistics. _931391
650			_aComputer Application in Social and Behavioral Sciences. _931392
650			_aComputer Modelling. _931393
650			_aComputer simulation. _931394
650			_aDigital humanities. _931395
650			_aDigital Humanities. _931396
650			_aMachine learning. _931397
650			_aMachine Learning. _931398
650			_aNatural language processing (Computer science). _931399
650			_aNatural Language Processing (NLP). _931400
650			_aSocial sciences _931401
856			_uhttps://doi.org/10.1007/978-3-031-26809-0
942			_cEBK _2ddc
999			_c15210 _d15210