TY  - GEN
AU  - McShane,Marjorie
AU  - Nirenburg,Sergei
TI  - Linguistics for the Age of AI
T2  - The MIT Press
SN  - 9780262363136
PY  - 2021///
CY  - Cambridge
PB  - The MIT Press
KW  - Искусственный интеллект
KW  - Natural language understanding
KW  - Computational semantics
KW  - Intelligent agents
KW  - Cognitive modelling
KW  - Artificial intelligence
KW  - Language-endowed intelligent agents
KW  - Natural language processing
KW  - Language-endowed intelligent agent systems
KW  - Linguistic and extralinguistic scope
KW  - Actionability
KW  - Explanation
KW  - Theory and methodology
KW  - Knowledge bases
KW  - Incrementality
KW  - Microtheories
KW  - Pre-semantic analysis
KW  - Anaphoric event coreference
KW  - Residual ambiguities
KW  - Incongruities
KW  - Underspecification
N1  - Open Access
N2  - A 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
UR  - https://directory.doabooks.org/handle/20.500.12854/78608
ER  -