Actually , Genuinely , Really , and Truly
A corpus-based Behavioral Profile study of near-synonymous adverbs
Bibliographic Data
| ID | 12621369 |
|---|---|
| Authors | Dilin Liu (0000-0002-8290-3734, University of Alabama, corresponding author), Maggie Espino |
| Year | 2012 |
| Volume | 17 |
| Issue | 2 |
| Pages | 198-228 |
| Publication date | 2012-11-26 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | International Journal of Corpus Linguistics (JOURNAL) |
| Journal identifiers | ISSN: 1384-6655 • E-ISSN: 1569-9811 |
| Publisher | John Benjamins Publishing Company (PUBLISHER • NL) |
| DOI | 10.1075/ijcl.17.2.03liu |
| OpenAlex | W2007874004 |
| Language | EN |
| Citations received | 6 |
Using the 400+ million-word Corpus of Contemporary American English (COCA) as data and a behavioral profile analysis approach, this study examines the semantic and usage differences among actually , genuinely , really , and truly , four near-synonymous adverbs notorious for their complex functional and syntactic usage patterns. In addition to examining the collocates of the adverbs (i.e. the common types of adjectives/verbs they each modify), the study also investigated the positions the adverbs typically occupy in the sentence structure and their functional implications. The data analysis yields an informative delineation of the internal semantic structure of the synonym set, which includes some interesting new findings compared with existing descriptions of the adverbs. The results also show the need for the BP approach to go beyond collocational analysis in the study of synonymous adverbs. Finally, the study also demonstrates the viability of a corpus-based BP approach in the study of synonymous adverbs
American English · Biology · Coca · Linguistics · Natural language processing · Sentence · Set (abstract data type · Synonym (taxonomy · Word (group theory · Computer Science · Language, Metaphor, and Cognition · Natural Language Processing Techniques · Philosophy · Psychology · Second Language Acquisition and Learning · Artificial Intelligence
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| Unique citing works | 6 |
|---|---|
| Citations per year | 0.6 |
| Citation span | 2016 - 2025 (10) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 6 |