cleantext

internal

Models for detecting concreteness in natural language. This package is built in support of Yeomans (2021) <doi:10.1016/j.obhdp.2020.10.008>, which reviews linguistic models of concreteness in several domains. Here, we provide an implementation of the best-performing domain-general model (from Brysbaert et al., (2014) <doi:10.3758/s13428-013-0403-5>) as well as two pre-trained models for the feedback and plan-making domains.

Mike Yeomans

doc2concrete

Measuring Concreteness in Natural Language

cleantext function

<dl><dt>text</dt>
<dd>character Vector of strings to clean.</dd>
<dt>language</dt>
<dd>character Language to use for cleaning. Default is "english".</dd>
<dt>punct</dt>
<dd>logical Should punctuation be kept as tokens? Default is TRUE.</dd>
<dt>stop.words</dt>
<dd>logical Should stop words be kept? default is TRUE.</dd>
<dt>number.words</dt>
<dd>logical Should numbers be converted to words? default is TRUE.</dd></dl>

Arguments

Text Cleaner — cleantext

<dl>

<dt>text</dt>
<dd>character Vector of strings to clean.</dd>


<dt>language</dt>
<dd>character Language to use for cleaning. Default is "english".</dd>


<dt>punct</dt>
<dd>logical Should punctuation be kept as tokens? Default is TRUE.</dd>


<dt>stop.words</dt>
<dd>logical Should stop words be kept? default is TRUE.</dd>


<dt>number.words</dt>
<dd>logical Should numbers be converted to words? default is TRUE.</dd>

</dl>

cleantext: Text Cleaner

Description

Usage

Value

Arguments