PEAS Perfect aspect

Biber A2 Xiao B27 MFTE PEAS Bohmann 51

Definition

Perfect aspect constructions (have/has/had + past participle).

Detection Rules

cql[lemma="have" & pos="VBP|VBZ|VBD"] [pos="VBN"]

Requires: lemma, pos

semgrex{pos:/VBN/}=main >aux {lemma:have}=aux

Requires: lemma, pos, dep

pybiber

Have as auxiliary (dep=aux). Trusts the dependency parser to identify perfect auxiliaries regardless of intervening adverbs or negation.

cql[lemma="have" & dep="aux"]

Requires: lemma, pos, dep

mfte

MFTE uses positional heuristics to detect perfect aspect: have/has/had followed by a past participle (VBN or mistagged VBD ending in -ed), allowing 0-2 intervening tokens (adverbs, negation, pronouns for inversion). Does not use dependency parsing. VB included for bare infinitive “have” after modals (e.g., “shouldn’t have done”). “got” excluded because MFTE tags “have got” as HGOT (not PEAS) unless followed by another participle (PGET, handled separately).

p1 
cql[lemma="have" & pos="VB|VBP|VBZ|VBD"] [pos="VBN" & word!="got"]
p2 
cql[lemma="have" & pos="VB|VBP|VBZ|VBD"] [pos="RB.*|PRP|CC|UH"] [pos="VBN" & word!="got"]
p3 
cql[lemma="have" & pos="VB|VBP|VBZ|VBD"] [pos="RB.*|PRP|CC|UH"] [pos="RB.*|PRP|CC|UH"] [pos="VBN" & word!="got"]
p4 
cql[lemma="have" & pos="VB|VBP|VBZ|VBD"] [pos="NN.*|PRP"] [pos="VBN" & word!="got"]
p5 
cql[lemma="have" & pos="VB|VBP|VBZ|VBD"] [word=".*ed" & pos="VBD"]
p6 
cql[lemma="have" & pos="VB|VBP|VBZ|VBD"] [pos="RB.*|PRP|CC|UH"] [word=".*ed" & pos="VBD"]
p7 
cql[lemma="have" & pos="VBG"] [pos="VBN" & word!="got"]
p8 
cql[lemma="have" & pos="VBG"] [pos="RB.*"] [pos="VBN" & word!="got"]
combine: p1 | p2 | p3 | p4 | p5 | p6 | p7 | p8

Requires: word, lemma, pos

Refines: VBN

Normalization

Per finite_verbs

Examples

He has been told before.

Source: le_foll_2024

Have you been on a student exchange?

Source: le_foll_2024

She’d already seen it.

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
  • bohmann_2019 — Bohmann, Axel (2019) : Variation in English Worldwide: Varieties and Genres in a Quantitative Perspective

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