particle_verbs_short: English particle verbs

In jasongraf1/VADIS: Variation-Based Distance & Similarity Modeling

English particle verbs

Description

Data describing the placement post-verbal particles in nine varieties of English contained in the International Corpus of English (ICE) and the Global Corpus of Web-based English (GloWbE).

Usage

particle_verbs_short

Format

A data frame with 11340 rows and 26 variables:

Variety

a factor with 9 levels coding the variety from which the token was taken

Corpus

a factor levels GloWbE and ICE coding the corpus from which the token was taken

Genre

a factor with 14 levels for the genres of texts in the corpora

Register

a factor with 5 levels online, spok.formal, spok.informal,writ.formal, writ.informal coding the register and mode of the text

Verb

a factor coding the verb, e.g. "pick"

Particle

a factor coding the particle, e.g. "up"

VerbPart

a factor coding the verb-particle, e.g. "pick up"

VerbForm

a factor coding the inflected form of the verb, e.g. "picks", "picked"

DirObject

a character vector coding the entire direct object NP

DirObjHead

a character vector coding the head noun of the direct object

Response

a factor with levels Continuous ("pick up the book") and Split ("pick the book up") coding the placement of the particle

DirObjWordLength

a numeric vector coding the number of words in the direct object

DirObjLettLength

a numeric vector coding the number of orthographic letters in the direct object

DirObjExprType

a factor with 4 levels: iprn (impersonal pronoun, "someone"); nc (common noun, "the bar of chocolate"), np (proper noun, "Mister Eto"); vp (verbal gerund, "reading The Times")

DirObjDefiniteness

a factor with levels def and indef coding the definiteness of the direct object

DirObjGivenness

a factor with levels given and new coding the discourse givenness of the direct object

DirObjConcreteness

a factor with levels Concrete and Nonconcrete coding the concreteness of the direct object

Semantics

a factor with levels compositional and non-compositional coding the semantic compositionality of the particle verb token

DirObjThematicity

a numeric vector coding the thematicity of the head noun direct object. Measured as the log-transformed normalized text frequency of the head noun.

DirectionalPP

a factor with levels no and yes coding the presence of a directional prepositional phrase following the VP, e.g. "picking up a big beach ball off the ground"

PrimeType

a factor with levels Continuous, Split, and none coding the placement of the particle in the previous token in the text. none = no prior particle verb tokens were found in the text.

Surprisal.P

a numeric vector coding the surprisal of the particle given the verb

Surprisal.V

a numeric vector coding the surprisal of the verb given the particle

CV.binary

a factor with levels CC (e.g. "put down") and other (e.g. "put away", "pay back", "throw away") coding the pattern of the final segment of the verb and initial segment of the particle

StressClash

a factor with levels no and yes coding whether the continuous variant results in a stress clash across the verb-particle boundary ("conNECT UP")

Rhythm

a numeric vector coding the eurhythmic distance of the observed token

...

Source

https://osf.io/x8vyw/

jasongraf1/VADIS documentation built on July 19, 2023, 10:26 p.m.