Ideal Words
| dc.contributor.author | Herbelot, Aurélie | |
| dc.contributor.author | Copestake, Ann | |
| dc.date.accessioned | 2021-12-16T13:22:59Z | |
| dc.date.available | 2021-12-16T13:22:59Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | 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. | de |
| dc.identifier.doi | 10.1007/s13218-021-00719-5 | |
| dc.identifier.pissn | 1610-1987 | |
| dc.identifier.uri | http://dx.doi.org/10.1007/s13218-021-00719-5 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/37803 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 35, No. 0 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.subject | Competence | |
| dc.subject | Distributional semantics | |
| dc.subject | Formal semantics | |
| dc.title | Ideal Words | de |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 290 | |
| gi.citation.startPage | 271 |