Measuring Concreteness in Natural Language
Cleaning weird encodings
Text Cleaner
Open-Domain Concreteness Dictionaries
Contraction Expander
Concreteness Scores
Doublestacker
Ngram Tokenizer
Overlap cleaner
Conditional Stemmer
Stemmer
Text Formatter
UK to US conversion
Feature Count Matcher
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.