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  1. Vector Semantics
    Published: 2023
    Publisher:  Springer Verlag, Singapore, Singapore

    This open access book introduces Vector semantics, which links the formal theory of word vectors to the cognitive theory of linguistics.The computational linguists and deep learning researchers who developed word vectors have relied primarily on the... more

     

    This open access book introduces Vector semantics, which links the formal theory of word vectors to the cognitive theory of linguistics.The computational linguists and deep learning researchers who developed word vectors have relied primarily on the ever-increasing availability of large corpora and of computers with highly parallel GPU and TPU compute engines, and their focus is with endowing computers with natural language capabilities for practical applications such as machine translation or question answering. Cognitive linguists investigate natural language from the perspective of human cognition, the relation between language and thought, and questions about conceptual universals, relying primarily on in-depth investigation of language in use.In spite of the fact that these two schools both have 'linguistics' in their name, so far there has been very limited communication between them, as their historical origins, data collection methods, and conceptual apparatuses are quite different. Vector semantics bridges the gap by presenting a formal theory, cast in terms of linear polytopes, that generalizes both word vectors and conceptual structures, by treating each dictionary definition as an equation, and the entire lexicon as a set of equations mutually constraining all meanings

     

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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Print
    ISBN: 9789811956065
    Edition: 1st ed. 2023
    Series: Cognitive Technologies
    Subjects: Artificial intelligence; COMPUTERS / Artificial Intelligence; COMPUTERS / Data Processing / Speech & Audio Processing; COMPUTERS / Expert Systems; Computational linguistics; Computer-Anwendungen in Kunst und Geisteswissenschaften; Computer-Anwendungen in den Sozial- und Verhaltenswissenschaften; Computerlinguistik und Korpuslinguistik; Expert systems / knowledge-based systems; Künstliche Intelligenz; LANGUAGE ARTS & DISCIPLINES / Library & Information Science; LANGUAGE ARTS & DISCIPLINES / Linguistics; Literature: history & criticism; Machine learning; Maschinelles Lernen; Natural language & machine translation; Natürliche Sprachen und maschinelle Übersetzung; Wissensbasierte Systeme, Expertensysteme
    Scope: 273 Seiten
    Notes:

    Interessenniveau: 06, Professional and scholarly: For an expert adult audience, including academic research. (06)

    Contents Preface............................................................... vii1 Foundations of non-compositionality................................. 1.1 Background ................................................... 1.2 Lexicographic principles ........................................ 1.3 The syntax of definitions ........................................ 1.4 The geometry of definitions...................................... 1.5 The algebra of definitions ....................................... 2 From morphology to syntax ........................................ 23 2.1 Lexical categories and subcategories .............................. 23 2.2 Bound morphemes ............................................. 25 2.3 Relations ..................................................... 30 2.4 Linking....................................................... 39 2.5 Naive grammar ................................................ 463 Time and space.................................................... 53 3.1 Space ........................................................ 54 3.2 Time ......................................................... 59 3.3 Indexicals, coercion ............................................ 62 3.4 Measure ...................................................... 654 Negation.......................................................... 69 4.1 Negation in the lexicon.......................................... 71 4.2 Quantifiers .................................................... 73 4.3 Negation in compositional constructions ........................... 74 4.4 Double negation ............................................... 77 4.5 Compositional quantifiers ....................................... 78 4.6 Disjunction ................................................... 80 4.7 Scope ambiguities.............................................. 81 4.8 Conclusions ................................................... 82 5 Valuations ........................................................ 83 5.1 Introduction ................................................... 83 5.2 The likeliness scale............................................. 84 5.3 Naive inference (likeliness update) ................................ 86 5.4 Learning...................................................... 89 5.5 Conclusions ................................................... 916 Modality ......................................................... 93 6.1 The deontic world .............................................. 93 6.2 Epistemic and autoepistemic logic ................................ 93 6.3 Defaults ...................................................... 937 Adjectives, gradience, implicature ................................... 95 7.1 Adjectives .................................................... 95 7.2 Gradience..................................................... 96 7.3 Implicature.................................................... 96 7.4 The elementary pieces .......................................... 97 7.5 The mechanism ................................................ 100 7.6 Memory ...................................................... 103 7.7 Conclusions ................................................... 1048 Trainability and real-world knowledge............................... 1078.1 Proper names.................................................. 107 8.2 Trainability ................................................... 1099 Dynamic embeddings ....................................

  2. Vector Semantics
    Published: 2023
    Publisher:  Springer Verlag, Singapore, Singapore

    This open access book introduces Vector semantics, which links the formal theory of word vectors to the cognitive theory of linguistics.The computational linguists and deep learning researchers who developed word vectors have relied primarily on the... more

     

    This open access book introduces Vector semantics, which links the formal theory of word vectors to the cognitive theory of linguistics.The computational linguists and deep learning researchers who developed word vectors have relied primarily on the ever-increasing availability of large corpora and of computers with highly parallel GPU and TPU compute engines, and their focus is with endowing computers with natural language capabilities for practical applications such as machine translation or question answering. Cognitive linguists investigate natural language from the perspective of human cognition, the relation between language and thought, and questions about conceptual universals, relying primarily on in-depth investigation of language in use.In spite of the fact that these two schools both have 'linguistics' in their name, so far there has been very limited communication between them, as their historical origins, data collection methods, and conceptual apparatuses are quite different. Vector semantics bridges the gap by presenting a formal theory, cast in terms of linear polytopes, that generalizes both word vectors and conceptual structures, by treating each dictionary definition as an equation, and the entire lexicon as a set of equations mutually constraining all meanings

     

    Export to reference management software   RIS file
      BibTeX file
    Content information
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Print
    ISBN: 9789811956096
    Edition: 1st ed. 2023
    Series: Cognitive Technologies
    Subjects: Artificial intelligence; COMPUTERS / Artificial Intelligence; COMPUTERS / Data Processing / Speech & Audio Processing; COMPUTERS / Expert Systems; Computational linguistics; Computer-Anwendungen in Kunst und Geisteswissenschaften; Computer-Anwendungen in den Sozial- und Verhaltenswissenschaften; Computerlinguistik und Korpuslinguistik; Expert systems / knowledge-based systems; Künstliche Intelligenz; LANGUAGE ARTS & DISCIPLINES / Library & Information Science; LANGUAGE ARTS & DISCIPLINES / Linguistics; Literature: history & criticism; Machine learning; Maschinelles Lernen; Natural language & machine translation; Natürliche Sprachen und maschinelle Übersetzung; Wissensbasierte Systeme, Expertensysteme
    Scope: 273 Seiten
    Notes:

    Interessenniveau: 06, Professional and scholarly: For an expert adult audience, including academic research. (06)

    Contents Preface............................................................... vii1 Foundations of non-compositionality................................. 1.1 Background ................................................... 1.2 Lexicographic principles ........................................ 1.3 The syntax of definitions ........................................ 1.4 The geometry of definitions...................................... 1.5 The algebra of definitions ....................................... 2 From morphology to syntax ........................................ 23 2.1 Lexical categories and subcategories .............................. 23 2.2 Bound morphemes ............................................. 25 2.3 Relations ..................................................... 30 2.4 Linking....................................................... 39 2.5 Naive grammar ................................................ 463 Time and space.................................................... 53 3.1 Space ........................................................ 54 3.2 Time ......................................................... 59 3.3 Indexicals, coercion ............................................ 62 3.4 Measure ...................................................... 654 Negation.......................................................... 69 4.1 Negation in the lexicon.......................................... 71 4.2 Quantifiers .................................................... 73 4.3 Negation in compositional constructions ........................... 74 4.4 Double negation ............................................... 77 4.5 Compositional quantifiers ....................................... 78 4.6 Disjunction ................................................... 80 4.7 Scope ambiguities.............................................. 81 4.8 Conclusions ................................................... 82 5 Valuations ........................................................ 83 5.1 Introduction ................................................... 83 5.2 The likeliness scale............................................. 84 5.3 Naive inference (likeliness update) ................................ 86 5.4 Learning...................................................... 89 5.5 Conclusions ................................................... 916 Modality ......................................................... 93 6.1 The deontic world .............................................. 93 6.2 Epistemic and autoepistemic logic ................................ 93 6.3 Defaults ...................................................... 937 Adjectives, gradience, implicature ................................... 95 7.1 Adjectives .................................................... 95 7.2 Gradience..................................................... 96 7.3 Implicature.................................................... 96 7.4 The elementary pieces .......................................... 97 7.5 The mechanism ................................................ 100 7.6 Memory ...................................................... 103 7.7 Conclusions ................................................... 1048 Trainability and real-world knowledge............................... 1078.1 Proper names.................................................. 107 8.2 Trainability ................................................... 1099 Dynamic embeddings ....................................