TIME Time adverbials

Biber B5 Xiao E71 MFTE TIME Bohmann 132

Definition

Adverbs and adverbial expressions of time: now, soon, recently, then, today, yesterday, etc.

Detection Rules

Default time adverbs rule. Refines RB and NN.

cql[word={words} & pos="RB"]
Word list (41 items)
after afterwards again ago already anymore anytime before beforehand briefly currently earlier early eventually formerly immediately initially instantly late lately later momentarily now nowadays originally overnight presently previously recently shortly simultaneously someday soon sooner subsequently suddenly today tomorrow tonight yesterday yet

Requires: word, pos

Refines: RB|NN

mfte

MFTE time adverbs in two groups: words like “after” and “before” are restricted to RB POS tag (they can also be prepositions or conjunctions), while unambiguous time adverbs match any POS. Some multi-word conditions not captured: “soon” excludes “as soon as”, “prior” requires following “to”, “so/thus far” is a separate two-word pattern.

p1 
cql[word={words_rb} & pos="RB"]
p2 
cql[word={words_any}]
combine: p1 | p2
Words: rb (6 items)
after before earlier early late later
Words: any (37 items)
afterwards ago already anymore anytime beforehand briefly currently eventually formerly immediately initially instantly lately momentarily now nowadays originally overnight presently previously recently shortly simultaneously someday soon sooner subsequently suddenly to-day to-morrow to-night today tomorrow tonight yesterday yet

Requires: word, pos

Refines: RB|NN

pybiber

pybiber blob regex _r\S* matches any R-family tag (RB, RBR, RBS, RP). E.g., “later” as RBR (comparative) would be missed by pos=“RB” alone.

cql[word={words} & pos="R.*"]
Word list (29 items)
again earlier early eventually formerly immediately initially instantly late lately later momentarily now nowadays once originally presently previously recently shortly simultaneously subsequently to-day to-morrow to-night today tomorrow tonight yesterday

Requires: word, pos

Normalization

Per finite_verbs

Examples

It will soon be possible.

Source: le_foll_2024

Now is the time.

Source: le_foll_2024

I haven’t come across any issues yet.

Source: le_foll_2024

Sources

  • biber_1988 — Biber, Douglas (1988) : Variation across Speech and Writing
  • mfte — Le Foll, Elen & Shakir, Muhammad (2023/2025) : Multi-Feature Tagger of English (MFTE) — Python version
  • pybiber — Brown, David West & Reinhart, Alex (2026) : pybiber — Python package for linguistic feature extraction and Multi-Dimensional Analysis
  • xiao_2009 — Xiao, Richard (2009) : Multidimensional analysis and the study of world Englishes
  • grieve_2023 — Grieve, Jack (2023) : Register variation explains stylometric authorship analysis
  • bohmann_2019 — Bohmann, Axel (2019) : Variation in English Worldwide: Varieties and Genres in a Quantitative Perspective

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