Specificity ratings for English data

A dataset of specificity ratings for English words is hereby presented, analyzed and discussed in relation with other collections of speaker-generated ratings, including concreteness. Both, specificity and concreteness are analyzed in their ability to explain decision latencies in lexical and semantic tasks, showing important individual contributions. Specificity ratings are collected through best-worst scaling method on the words included in the ANEW dataset (Bradley and Lang in Affective norms for English words (ANEW): instruction manual and affective ratings (Tech. Rep.). Technical report C-1, the center for research in psychophysiology, 1999), chosen for its compatibility with many other collections of rating resources, and for its comparability with Italian specificity data (Bolognesi and Caselli in Behav Res Methods 55(7):3531-3548, 2023), allowing for cross-linguistic comparisons. Results suggest that specificity plays an important role in word processing and the importance of taking specificity into consideration when investigating concreteness effects.

Authors

Institutions

Publication Details

Journal
Cognitive Processing
Published
2024-11-08
DOI
https://doi.org/10.1007/s10339-024-01239-4
Citations
6
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
2.08

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Specificity ratings for English data

Andrea Amelio Ravelli, Marianna Bolognesi, Tommaso Caselli
6 citations
Cognitive Processing
Natural Language Processing Techniques
2.08
article

Specificity ratings for English data

Andrea Amelio Ravelli, Marianna Bolognesi, Tommaso Caselli
article en
6 citations

Abstract

A dataset of specificity ratings for English words is hereby presented, analyzed and discussed in relation with other collections of speaker-generated ratings, including concreteness. Both, specificity and concreteness are analyzed in their ability to explain decision latencies in lexical and semantic tasks, showing important individual contributions. Specificity ratings are collected through best-worst scaling method on the words included in the ANEW dataset (Bradley and Lang in Affective norms for English words (ANEW): instruction manual and affective ratings (Tech. Rep.). Technical report C-1, the center for research in psychophysiology, 1999), chosen for its compatibility with many other collections of rating resources, and for its comparability with Italian specificity data (Bolognesi and Caselli in Behav Res Methods 55(7):3531-3548, 2023), allowing for cross-linguistic comparisons. Results suggest that specificity plays an important role in word processing and the importance of taking specificity into consideration when investigating concreteness effects.

Cognitive ProcessingVol. 26(2)
University of Groningen (NL), University of Bologna (IT)
HORIZON EUROPE European Research Council
Peace, Justice and strong institutions
Openalex Percentile: Top 11%
Natural Language Processing Techniques
2.08
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.