| 000 | 03741nam a22006255i 4500 | ||
|---|---|---|---|
| 001 | 978-981-19-5607-2 | ||
| 003 | DE-He213 | ||
| 005 | 20240507154520.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 221206s2023 si | s |||| 0|eng d | ||
| 020 |
_a9789811956072 _9978-981-19-5607-2 |
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| 024 | 7 |
_a10.1007/978-981-19-5607-2 _2doi |
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| 050 | 4 | _aQA76.9.N38 | |
| 072 | 7 |
_aUYQL _2bicssc |
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_aCOM073000 _2bisacsh |
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_a006.35 _223 |
| 100 | 1 |
_aKornai, András. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 245 | 1 | 0 |
_aVector Semantics _h[electronic resource] / _cby András Kornai. |
| 250 | _a1st ed. 2023. | ||
| 264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2023. |
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| 300 |
_aXVI, 273 p. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aCognitive Technologies, _x2197-6635 |
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| 505 | 0 | _aChapter 1.Foundations of non-compositionality -- Chapter 2. From morphology to syntax -- Chapter 3.Time and space -- Chapter 4. Negation -- Chapter 5.Valuations and learnability -- Chapter 6.Modality -- Chapter 7.Adjectives, gradience, implicature -- Chapter 8.Trainability and real-world knowledge -- Chapter 9. Applications. | |
| 506 | 0 | _aOpen Access | |
| 520 | _aThis 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. . | ||
| 650 | 0 | _aNatural language processing (Computer science). | |
| 650 | 0 | _aComputational linguistics. | |
| 650 | 0 | _aArtificial intelligence. | |
| 650 | 0 | _aMachine learning. | |
| 650 | 0 | _aExpert systems (Computer science). | |
| 650 | 0 | _aDigital humanities. | |
| 650 | 1 | 4 | _aNatural Language Processing (NLP). |
| 650 | 2 | 4 | _aComputational Linguistics. |
| 650 | 2 | 4 | _aArtificial Intelligence. |
| 650 | 2 | 4 | _aMachine Learning. |
| 650 | 2 | 4 | _aKnowledge Based Systems. |
| 650 | 2 | 4 | _aDigital Humanities. |
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811956065 |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811956089 |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811956096 |
| 830 | 0 |
_aCognitive Technologies, _x2197-6635 |
|
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-19-5607-2 |
| 912 | _aZDB-2-SCS | ||
| 912 | _aZDB-2-SXCS | ||
| 912 | _aZDB-2-SOB | ||
| 999 |
_c37441 _d37441 |
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