| 000 | 03688naaaa2200589uu 4500 | ||
|---|---|---|---|
| 003 | oapen | ||
| 005 | 20230518163635.0 | ||
| 006 | m o d | ||
| 007 | cr|mn|---annan | ||
| 008 | 20220221s2021 engx |||||o ||||eng|| d | ||
| 020 | _a9780262363136 | ||
| 020 | _a9780262045582 | ||
| 040 |
_aoapen _coapen |
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| 041 | 0 | _aeng | |
| 042 | _adc | ||
| 080 | _a004 | ||
| 100 | 1 |
_aMcShane, Marjorie _4auth |
|
| 245 | 1 | 0 | _aLinguistics for the Age of AI |
| 260 |
_aCambridge _bThe MIT Press _c2021 |
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| 300 | _a1 electronic resource (448 p.) | ||
| 490 | 1 | _aThe MIT Press | |
| 506 | 0 |
_aOpen Access _2star _fUnrestricted online access |
|
| 520 | _aA human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems. One of the original goals of artificial intelligence research was to endow intelligent agents with human-level natural language capabilities. Recent AI research, however, has focused on applying statistical and machine learning approaches to big data rather than attempting to model what people do and how they do it. In this book, Marjorie McShane and Sergei Nirenburg return to the original goal of recreating human-level intelligence in a machine. They present a human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems that emphasizes meaning—the deep, context-sensitive meaning that a person derives from spoken or written language. With Linguistics for the Age of AI, McShane and Nirenburg offer a roadmap for creating language-endowed intelligent agents (LEIAs) that can understand,explain, and learn. They describe the language-understanding capabilities of LEIAs from the perspectives of cognitive modeling and system building, emphasizing “actionability”—which involves achieving interpretations that are sufficiently deep, precise, and confident to support reasoning about action. After detailing their microtheories for topics such as semantic analysis, basic coreference, and situational reasoning, McShane and Nirenburg turn to agent applications developed using those microtheories and evaluations of a LEIA's language understanding capabilities. McShane and Nirenburg argue that the only way to achieve human-level language understanding by machines is to place linguistics front and center, using statistics and big data as contributing resources. They lay out a long-term research program that addresses linguistics and real-world reasoning together, within a comprehensive cognitive architecture. | ||
| 540 |
_aCreative Commons _fby-nc-nd/4.0 _2cc |
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| 546 | _aEnglish | ||
| 650 | 0 |
_aИскусственный интеллект _94518 |
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| 653 | _aNatural language understanding | ||
| 653 | _aComputational semantics | ||
| 653 | _aIntelligent agents | ||
| 653 | _aCognitive modelling | ||
| 653 | _aArtificial intelligence | ||
| 653 | _aLanguage-endowed intelligent agents | ||
| 653 | _aNatural language processing | ||
| 653 | _aLanguage-endowed intelligent agent systems | ||
| 653 | _aLinguistic and extralinguistic scope | ||
| 653 | _aActionability | ||
| 653 | _aExplanation | ||
| 653 | _aTheory and methodology | ||
| 653 | _aKnowledge bases | ||
| 653 | _aIncrementality | ||
| 653 | _aMicrotheories | ||
| 653 | _aPre-semantic analysis | ||
| 653 | _aAnaphoric event coreference | ||
| 653 | _aResidual ambiguities | ||
| 653 | _aIncongruities | ||
| 653 | _aUnderspecification | ||
| 700 | 1 |
_aNirenburg, Sergei _4auth |
|
| 830 |
_94582 _aThe MIT Press |
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| 856 | 4 | 0 |
_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/78608 _70 _zDescription |
| 909 |
_c4 _dDarya Shvetsova |
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| 942 |
_2udc _cEE |
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| 999 |
_c6539 _d6539 |
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