PEAS YAML source
code: PEAS
biber_number: A2
xiao_number: B27
mfte_code: PEAS
bohmann_number: 51
name: Perfect aspect
definition: >-
Perfect aspect constructions (have/has/had + past participle).
normalization: finite_verbs
detection:
- requires:
- lemma
- pos
cql: '[lemma="have" & pos="VBP|VBZ|VBD"] [pos="VBN"]'
- requires:
- lemma
- pos
- dep
semgrex: '{pos:/VBN/}=main >aux {lemma:have}=aux'
- source: pybiber
requires:
- lemma
- pos
- dep
cql: '[lemma="have" & dep="aux"]'
description: >-
Have as auxiliary (dep=aux). Trusts the dependency parser to identify
perfect auxiliaries regardless of intervening adverbs or negation.
- source: mfte
requires:
- word
- lemma
- pos
parts:
p1:
cql: '[lemma="have" & pos="VB|VBP|VBZ|VBD"] [pos="VBN" & word!="got"]'
anchor: last
p2:
cql: '[lemma="have" & pos="VB|VBP|VBZ|VBD"] [pos="RB.*|PRP|CC|UH"] [pos="VBN" & word!="got"]'
anchor: last
p3:
cql: '[lemma="have" & pos="VB|VBP|VBZ|VBD"] [pos="RB.*|PRP|CC|UH"] [pos="RB.*|PRP|CC|UH"] [pos="VBN" & word!="got"]'
anchor: last
p4:
cql: '[lemma="have" & pos="VB|VBP|VBZ|VBD"] [pos="NN.*|PRP"] [pos="VBN" & word!="got"]'
anchor: last
p5:
cql: '[lemma="have" & pos="VB|VBP|VBZ|VBD"] [word=".*ed" & pos="VBD"]'
anchor: last
p6:
cql: '[lemma="have" & pos="VB|VBP|VBZ|VBD"] [pos="RB.*|PRP|CC|UH"] [word=".*ed" & pos="VBD"]'
anchor: last
p7:
cql: '[lemma="have" & pos="VBG"] [pos="VBN" & word!="got"]'
anchor: last
p8:
cql: '[lemma="have" & pos="VBG"] [pos="RB.*"] [pos="VBN" & word!="got"]'
anchor: last
combine: "p1 | p2 | p3 | p4 | p5 | p6 | p7 | p8"
refines: VBN
description: >-
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).
examples:
- text: He _has been_ told before.
source: le_foll_2024
- text: _Have_ you _been_ on a student exchange?
source: le_foll_2024
- text: _She'd_ already _seen_ it.
source: le_foll_2024
sources:
- biber_1988
- mfte
- pybiber
- xiao_2009
- bohmann_2019