Ideal Words: A Vector-Based Formalisation of Semantic Competence
| Autor: | Ann Copestake, Aurélie Herbelot |
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| Přispěvatelé: | Herbelot, A [0000-0002-4353-5908], Apollo - University of Cambridge Repository |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
050101 languages & linguistics
Competence Distributional semantics Formal semantics Ideal (set theory) Theoretical computer science Computer science 05 social sciences 02 engineering and technology Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Competence (human resources) |
| Popis: | Funder: Università degli Studi di Trento In this theoretical paper, we consider the notion of semantic competence and its relation to general language understanding—one of the most sough-after goals of Artificial Intelligence. We come back to three main accounts of competence involving (a) lexical knowledge; (b) truth-theoretic reference; and (c) causal chains in language use. We argue that all three are needed to reach a notion of meaning in artificial agents and suggest that they can be combined in a single formalisation, where competence develops from exposure to observable performance data. We introduce a theoretical framework which translates set theory into vector-space semantics by applying distributional techniques to a corpus of utterances associated with truth values. The resulting meaning space naturally satisfies the requirements of a causal theory of competence, but it can also be regarded as some ‘ideal’ model of the world, allowing for extensions and standard lexical relations to be retrieved. |
| Databáze: | OpenAIRE |
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