He Said, She Said: Gender in the ACL Anthology
By Adam Vogel, Dan Jurafsky. 2012
64 citations recorded by the source corpus.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Hannaneh Hajishirzi
- Mohit Bansal
- Christopher D. Manning
- Luke S. Zettlemoyer
- Percy Liang
- Eduard H. Hovy
- Dan Jurafsky
- Jianfeng Gao
Citations in the corpus
- Reusable workflows for gender prediction (2018, methodology): “…attempts at AP that covered gender identification started with Kop-pel et al. (2002), who used parts of the BNC, but continued on other corpora, such as the ACL corpus of scientific papers (Vogel and Jurafsky, 2012) or UGC corpora, esp. in the context of PAN shared tasks (Rangel et al., 2017).”
- Deterministic Coreference Resolution Based on Entity-Centric, Precision-Ranked Rules (2013, methodology): “…we used the following additional resources: (a) a hand-built list of genders of first names that we created, incorporating frequent names from census lists and other sources (Vogel and Jurafsky 2012) (b) an animacy list (Ji and Lin 2009), (c) a country and state gazetteer, and (d) a demonym list.”
- Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction (2018, methodology): “…Kas, 2011; Gabor et al., 2016; Sim et al., 2012; Do et al., 2013; Jaidka et al., 2014; Abu-Jbara and Radev, 2011), analyzing research community (Vogel and Jurafsky, 2012; Anderson et al., 2012), and unsupervised methods for extracting sci-entific entities and relations (Gupta and Manning, 2011;…”
- Gender Gap in Natural Language Processing Research: Disparities in Authorship and Citations (2020, methodology): “Our work examines a much larger set of NLP papers (1965–2019), re-examines some of the questions raised in Vogel and Jurafsky (2012), and explores several new questions, especially on first author gender and disparities in citation.”
- (Male, Bachelor) and (Female, Ph.D) have different connotations: Parallelly Annotated Stylistic Language Dataset with Multiple Personas (2019, background): “…applications us-ing stylistic features have been studied such as poetry generation (Ghazvininejad et al., 2017), stylometry with demographic information (Ver-hoeven and Daelemans, 2014), modeling style bias (Vogel and Jurafsky, 2012) and modeling bio-graphic attributes (Garera and Yarowsky, 2009).”