Lexical typology through similarity semantics
Toward a semantic map of motion verbs
Dados Bibliográficos
| ID | 3988676 |
|---|---|
| Autores | Bernhard Wälchli (0000-0002-0256-6855, Max Planck Society, autor correspondente), Michael Cysouw (0000-0003-3168-4946, Ludwig-Maximilians-Universität München) |
| Ano | 2012 |
| Volume | 50 |
| Fascículo | 3 |
| Data de publicação | 2012-01-18 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | Linguistics (JOURNAL) |
| Identificadores do periódico | ISSN: 0024-3949 • E-ISSN: 1613-396X |
| Editora | Walter de Gruyter GmbH (PUBLISHER • DE) |
| DOI | 10.1515/ling-2012-0021 |
| OpenAlex | W1992305141 |
| Idioma | EN |
| Citações recebidas | 35 |
| Referências citadas | 20 |
This paper discusses a multidimensional probabilistic semantic map of lexical motion verb stems based on data collected from parallel texts (viz. translations of the Gospel according to Mark) for 100 languages from all continents. The crosslinguistic diversity of lexical semantics in motion verbs is illustrated in detail for the domain of `go', `come', and `arrive' type contexts. It is argued that the theoretical bases underlying probabilistic semantic maps from exemplar data are the isomorphism hypothesis (given any two meanings and their corresponding forms in any particular language, more similar meanings are more likely to be expressed by the same form in any language), similarity semantics (similarity is more basic than identity), and exemplar semantics (exemplar meaning is more fundamental than abstract concepts)
Lexical item · Lexical Semantics · Lexicology · Linguistics · Motion (physics) · Natural language processing · Semantic similarity · Semantics (computer science) · Similarity (geometry) · Verb · Artificial Intelligence · Categorization, perception, and language · Computer Science · Language, Metaphor, and Cognition · Lexicography and Language Studies · Philosophy
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| Obras citantes distintas | 35 |
|---|---|
| Citações por ano | 2,92 |
| Intervalo de citações | 2014 - 2026 (13) |
| Velocidade de citação | current |
| Altamente citado | Não |
| Tipos de citação | Neutras: 30 |