adviceModel

This model was pre-trained on 3289 examples of feedback on different tasks (e.g. writing a cover letter, boggle, workplace annual reviews). All of those documents were annotated by research assistants for concreteness, and this model simulates those annotations on new documents.
Model pre-trained on advice data.

datasets

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

adviceModel function

Format

<dl><dt>texts</dt>
<dd>character A vector of texts, each of which will be tallied for concreteness.</dd>
<dt>num.mc.cores</dt>
<dd>numeric number of cores for parallel processing - see parallel::detectCores(). Default is 1.</dd></dl>

Arguments

Pre-trained Concreteness Detection Model for Advice — adviceModel

<dl>

<dt>texts</dt>
<dd>character A vector of texts, each of which will be tallied for concreteness.</dd>


<dt>num.mc.cores</dt>
<dd>numeric number of cores for parallel processing - see parallel::detectCores(). Default is 1.</dd>

</dl>

adviceModel: Pre-trained Concreteness Detection Model for Advice

Description

Usage

Value

Format

Arguments