Using Corpora to Analyze Gender

By Paul Baker. 2014

148 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Gender Studies, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/229286a8d9b00f9632ca62dc602bee09d9241743

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Isabelle Augenstein
2. Lawrence Wolf-Sonkin
3. Ryan Cotterell
4. Hanna M. Wallach
5. Ben Verhoeven
6. Senja Pollak
7. Alexander Miserlis Hoyle
8. Adina Williams

In Gender Studies

1. Isabelle Augenstein
2. Lawrence Wolf-Sonkin
3. Ryan Cotterell
4. Hanna M. Wallach
5. Alexander Miserlis Hoyle
6. Adina Williams

In Human-Computer Interaction

1. Lawrence Wolf-Sonkin
2. Isabelle Augenstein
3. Ryan Cotterell
4. Hanna M. Wallach
5. Ben Verhoeven
6. Alexander Miserlis Hoyle

Citations in the corpus

• Gender Profiling for Slovene Twitter communication: the Influence of Gender Marking, Content and Style (2017, background): “Another characteristic distinctive of the male class is non-alphabetical symbols including symbols for euro ( e ) and percent (%), and numerals (as digits) – the latter were also found to be more indicative of male authors and speakers in an English corpus of various genres (Newman et al., 2008) and”
• Unsupervised Discovery of Gendered Language through Latent-Variable Modeling (2019, methodology): “…on gendered language has focused on laboratory studies and smaller corpora (McKee and Sherriffs, 1957; Williams and Bennett, 1975; Baker, 2005); however, more recent work has begun to focus on larger-scale datasets (Pearce, 2008; Caldas-Coulthard and Moon, 2010; Baker, 2014; Norberg, 2016).”