What Men Say, What Women Hear: Finding Gender-Specific Meaning Shades

The authors examine the problem of gender discrimination and attempt to move beyond the typical surface-level text classification approach by identifying differences between genders in the ways they use the same words. They present several experiments using data from a large collection of blogs authored by men and women, and they report results for a new task of "gender-based word disambiguation" for a set of over 350 words.

[1]  John D. Burger,et al.  Discriminating Gender on Twitter , 2011, EMNLP.

[2]  Carlo Strapparava,et al.  WordNet Affect: an Affective Extension of WordNet , 2004, LREC.

[3]  Hwee Tou Ng,et al.  An Empirical Evaluation of Knowledge Sources and Learning Algorithms for Word Sense Disambiguation , 2002, EMNLP.

[4]  Walter Daelemans,et al.  Predicting age and gender in online social networks , 2011, SMUC '11.

[5]  Tejashri Inadarchand Jain,et al.  Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2010 .

[6]  Shlomo Argamon,et al.  Automatically Categorizing Written Texts by Author Gender , 2002, Lit. Linguistic Comput..

[7]  Arjun Mukherjee,et al.  Improving Gender Classification of Blog Authors , 2010, EMNLP.

[8]  R. Lakoff.  Language and woman's place , 1973, Language in Society.

[9]  Mari Ostendorf,et al.  A Quantitative Analysis of Lexical Differences Between Genders in Telephone Conversations , 2005, ACL.

[10]  David A. Huffaker,et al.  Gender, Identity, and Language Use in Teenage Blogs , 2006, J. Comput. Mediat. Commun..

[11]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[12]  Dan Klein,et al.  Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network , 2003, NAACL.

[13]  Dong Nguyen,et al.  Why Gender and Age Prediction from Tweets is Hard: Lessons from a Crowdsourcing Experiment , 2014, COLING.

[14]  David Yarowsky,et al.  Classifying latent user attributes in twitter , 2010, SMUC '10.

[15]  Yejin Choi,et al.  Gender Attribution: Tracing Stylometric Evidence Beyond Topic and Genre , 2011, CoNLL.

[16]  Owen Rambow,et al.  Staying on Topic: An Indicator of Power in Political Debates , 2014, EMNLP.

[17]  David Yarowsky,et al.  Improving Gender Prediction of Social Media Users via Weighted Annotator Rationales , 2014 .

[18]  Hugo Liu,et al.  Of Men, Women, and Computers: Data-driven Gender Modeling for Improved User Interfaces , 2022 .

[19]  John C. Paolillo,et al.  Gender and genre variation in weblogs , 2006 .

[20]  Загоровская Ольга Владимировна,et al.  Исследование влияния пола и психологических характеристик автора на количественные параметры его текста с использованием программы Linguistic Inquiry and Word Count , 2015 .

[21]  Shlomo Argamon,et al.  Effects of Age and Gender on Blogging , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.

[22]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..