Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Ruth Pogacar

dblp:228/5554 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Speech recognition and synthesis · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Speech recognition and synthesis
sound symbolism
0.312018
Is Nike female? Exploring the role of sound symbolism in predicting brand name gender · EMNLP 2018

Methods — techniques the papers use, named apart from their topics

supervised classification · 0.7linguistic feature extraction · 0.7
YearPublicationVenuePosition
2018 Is Nike female? Exploring the role of sound symbolism in predicting brand name gender
abstract
Are brand names such as Nike female or male?Previous research suggests that the sound of a person's first name is associated with the person's gender, but no research has tried to use this knowledge to assess the gender of brand names.We present a simple computational approach that uses sound symbolism to address this open issue.Consistent with previous research, a model trained on various linguistic features of name endings predicts human gender with high accuracy.Applying this model to a data set of over a thousand commerciallytraded brands in 17 product categories, our results reveal an overall bias toward male names, cutting across both male-oriented product categories as well as female-oriented categories.In addition, we find variation within categories, suggesting that firms might be seeking to imbue their brands with differentiating characteristics as part of their competitive strategy.
Sridhar Moorthy, Ruth Pogacar, Samin Khan, Yang Xu 0023
EMNLP2