A corpus-based analysis of textual genres in the administration domain
Bibliographic Data
| ID | 5966079 |
|---|---|
| Authors | Iria Da Cunha (0000-0002-7707-1574, Universidad Nacional de Educación a Distancia, corresponding author), M Amor Montané (Universitat Pompeu Fabra), M Amor Montané March (0000-0001-8910-2297, Universitat Pompeu Fabra) |
| Year | 2020 |
| Volume | 22 |
| Issue | 1 |
| Pages | 3-31 |
| Publication date | 2020-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Discourse Studies (JOURNAL) |
| Journal identifiers | ISSN: 1461-4456 • E-ISSN: 1461-7080 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/1461445619887538 |
| OpenAlex | W2990217604 |
| Language | EN |
| Citations received | 5 |
| References cited | 16 |
Laymen occasionally have to write texts in specialized domains, such as when interacting with the public administration. However, in Spanish, most studies related to administrative texts have focused on texts written by the administration for laymen rather than texts that average citizens write to the administration. Against this backdrop, this article aims to carry out the following: (a) a linguistic analysis on a corpus of Spanish-language texts from five textual genres written by laymen and addressed to the public administration (allegation, cover letter, letter of complaint, claim, and application) on the textual, lexical and discourse levels; (b) a contrastive analysis that applies statistical techniques to quantitative data to identify significant differences among these textual genres. The results show a detailed and comprehensive understanding of the characteristics of each genre. Also, the statistical results indicate that there are significant differences among some of the analyzed genres
Complaint · Corpus linguistics · Linguistics · Natural language processing · Political science · Statistical analysis · Computer Science · History · Law · linguistics and terminology studies · Spanish Linguistics and Language Studies · Text Readability and Simplification
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| Unique citing works | 5 |
|---|---|
| Citations per year | 0,83 |
| Citation span | 2020 - 2024 (5) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 4 |