000 03322nam a2200361Ia 4500
000 04419nam a22003975i 4500
001 978-3-031-22739-4
003 DE-He213
005 20240319120757.0
007 cr nn 008mamaa
008 230103s2023 sz | s |||| 0|eng d
020 _a9783031227394
_9978-3-031-22739-4
082 _a6.35
100 _aHausser, Roland.
_928410
245 _aOntology of Communication
_cby Roland Hausser.
_h[electronic resource] :
250 _a1st ed. 2023.
260 _aCham
_bSpringer Nature Switzerland
_c2023
300 _aXVI, 258 p. 1 illus.
_bonline resource.
520 _aThe book gives a comprehensive discussion of Database Semantics (DBS) as an agent-based data-driven theory of how natural language communication essentially works. In language communication, agents switch between speak mode, driven by cognition-internal content (input) resulting in cognition-external raw data (e.g. sound waves or pixels, which have no meaning or grammatical properties but can be measured by natural science), and hear mode, driven by the raw data produced by the speaker resulting in cognition-internal content. The motivation is to compare two approaches for an ontology of communication: agent-based data-driven vs. sign-based substitution-driven. Agent-based means: design of a cognitive agent with (i) an interface component for converting raw data into cognitive content (recognition) and converting cognitive content into raw data (action), (ii) an on-board, content-addressable memory (database) for the storage and content retrieval, (iii) separate treatments of the speak and the hear mode. Data-driven means: (a) mapping a cognitive content as input to the speak-mode into a language-dependent surface as output, (b) mapping a surface as input to the hear-mode into a cognitive content as output. Oppositely, sign-based means: no distinction between speak and hear mode, whereas substitution-driven means: using a single start symbol as input for generating infinitely many outputs, based on substitutions by rewrite rules. Collecting recent research of the author, this beautiful, novel and original exposition begins with an introduction to DBS, makes a linguistic detour on subject/predicate gapping and slot-filler repetition, and moves on to discuss computational pragmatics, inference and cognition, grammatical disambiguation and other related topics. The book is mostly addressed to experts working in the field of computational linguistics, as well as to enthusiasts interested in the history and early development of this subject, starting with the pre-computational foundations of theoretical computer science and symbolic logic in the 30s.
650 _aArtificial intelligence.
_928411
650 _aArtificial Intelligence.
_928412
650 _aComputational linguistics.
_928413
650 _aComputational Linguistics.
_928414
650 _aExpert systems (Computer science).
_928415
650 _aKnowledge Based Systems.
_928416
650 _aMachine learning.
_928417
650 _aMachine Learning.
_928418
650 _aNatural language processing (Computer science).
_928419
650 _aNatural Language Processing (NLP).
_928420
650 _aSymbolic AI.
_928421
856 _uhttps://doi.org/10.1007/978-3-031-22739-4
942 _cEBK
_2ddc
999 _c14957
_d14957