Automatically Identifying the Source Words of Lexical Blends in English
Dados Bibliográficos
| ID | 12156331 |
|---|---|
| Autores | Paul F Cook (0000-0001-7791-9072, University of Toronto, autor correspondente), Paul Cook (0000-0002-8586-4865, University of Toronto, autor correspondente), Suzanne Stevenson (0009-0007-6884-9958, University of Toronto) |
| Ano | 2010 |
| Volume | 36 |
| Fascículo | 1 |
| Páginas | 129-149 |
| Data de publicação | 2010-01-11 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | Computational Linguistics (JOURNAL) |
| Identificadores do periódico | ISSN: 0891-2017 • E-ISSN: 1530-9312 |
| Editora | Association for Computational Linguistics (PUBLISHER • US) |
| DOI | 10.1162/coli.2010.36.1.36104 |
| OpenAlex | W2077818552 |
| Idioma | EN |
| Citações recebidas | 5 |
| Referências citadas | 10 |
Newly coined words pose problems for natural language processing systems because they are not in a system's lexicon, and therefore no lexical information is available for such words. A common way to form new words is lexical blending, as in cosmeceutical, a blend of cosmetic and pharmaceutical. We propose a statistical model for inferring a blend's source words drawing on observed linguistic properties of blends; these properties are largely based on the recognizability of the source words in a blend. We annotate a set of 1,186 recently coined expressions which includes 515 blends, and evaluate our methods on a 324-item subset. In this first study of novel blends we achieve an accuracy of 40% on the task of inferring a blend's source words, which corresponds to a reduction in error rate of 39% over an informed baseline. We also give preliminary results showing that our features for source word identification can be used to distinguish blends from other kinds of novel words
Identification (biology · Lexicon · Linguistics · Natural language processing · Programming language · Set (abstract data type · Source text · Task (project management · Word (group theory · Authorship Attribution and Profiling · Computer Science · Natural Language Processing Techniques · Topic Modeling · Artificial Intelligence
| Obras citantes distintas | 5 |
|---|---|
| Citações por ano | 0,56 |
| Intervalo de citações | 2017 - 2023 (7) |
| Velocidade de citação | historical |
| Altamente citado | Não |
| Tipos de citação | Neutras: 4 |