Abstract
It is widely assumed that the frequency adverb always combined with the progressive aspect is typically used in negative evaluations expressing irritation, i.e., complaints. Adopting a cognitive-functional approach, I test this claim across six genres of Present Day English. Always progressives were coded according to their functions: Describe (neutral), Complain (negative), Lament (negative), or Praise (positive). Neutral, rather than negative, functions predominated in all genres, although negative functions outnumbered positive functions. I relate the former finding to the propensity of always progressives to act similarly to the simple aspect and the latter to a cognitive phenomenon called the negativity bias.
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Artikel in diesem Heft
- Frontmatter
- Annotating Speaker Stance in Discourse: The Brexit Blog Corpus
- ΔP as a measure of collocation strength
- Assessing theory with practice: an evaluation of two aspectual-semantic classification models of gerundive nominalizations
- Verb-argument constructions in advanced L2 English learner production: Insights from corpora and verbal fluency tasks
- Discourse functions of always progressives: Beyond complaining
- Pitting corpus-based classification models against each other: a case study for predicting constructional choice in written Estonian
- Using token-based semantic vector spaces for corpus-linguistic analyses: From practical applications to tests of theoretical claims
Artikel in diesem Heft
- Frontmatter
- Annotating Speaker Stance in Discourse: The Brexit Blog Corpus
- ΔP as a measure of collocation strength
- Assessing theory with practice: an evaluation of two aspectual-semantic classification models of gerundive nominalizations
- Verb-argument constructions in advanced L2 English learner production: Insights from corpora and verbal fluency tasks
- Discourse functions of always progressives: Beyond complaining
- Pitting corpus-based classification models against each other: a case study for predicting constructional choice in written Estonian
- Using token-based semantic vector spaces for corpus-linguistic analyses: From practical applications to tests of theoretical claims