1 / 22

Of Men, Women, and Computer: Data Driven Gender Modeling for Improved User Interfaces

Advisor: Hsin-Hsi Chen Speaker: Sheng-Chung Yen Date: 2007/04/09. Of Men, Women, and Computer: Data Driven Gender Modeling for Improved User Interfaces. Hugo Liu, Rada Mihalcea MIT, University of North Texas. Introduction.

inari
Download Presentation

Of Men, Women, and Computer: Data Driven Gender Modeling for Improved User Interfaces

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. NTU Natural Language Processing Lab. Advisor: Hsin-Hsi Chen Speaker: Sheng-Chung Yen Date: 2007/04/09 Of Men, Women, and Computer:Data Driven Gender Modeling for Improved User Interfaces Hugo Liu, RadaMihalcea MIT, University of North Texas

  2. NTU Natural Language Processing Lab. Introduction • Men and Women think and feel differently, and perceive, value and understand the world in their own ways. • Contributions: • They describe a corpus-based approach to gender modeling. • They build GENGERLENS - a novel intelligent news filtering system that customizes news based on the gender of its reader.

  3. NTU Natural Language Processing Lab. Data • Corpus • Blogspot, LiveJournal, and MSN-Space • 75000 male blog entries and 75000 female blog entries • With blogger profile • 2006.07.27 or 28

  4. NTU Natural Language Processing Lab. Feature Scoring

  5. NTU Natural Language Processing Lab. Dimensions of the gender space • Time • Food • Color • Size • Socialness • Affect

  6. NTU Natural Language Processing Lab. Time • relative-time expressions • such as ”last week” • concrete-time expressions • such as ”Wednesday”

  7. NTU Natural Language Processing Lab. • Women : here-and-now, from ”last weekend” through to ”this weekend.” • Men are more likely to focus on events of the past and future months and years. • Feminine writing dominates the days-of-the-week. • Masculine writing prefers to focus on months-of the-year.

  8. NTU Natural Language Processing Lab.

  9. NTU Natural Language Processing Lab. Food • Their experiment was to utilize the ontology of food terms from WordNet. • Feminine: Sweets and healthy foods • Masculine: liquids and hearty foods • Women paid more attention to the details of food.

  10. NTU Natural Language Processing Lab.

  11. NTU Natural Language Processing Lab. Color • They started with the widely-used X11 color lexicon.(http://en.wikipedia.org/wiki/List_of_colors) • color order – a concept in color theory which prescribes every color as being either primary, secondary, tertiary (3rd order), quaternary (4th order), and so on.

  12. NTU Natural Language Processing Lab.

  13. NTU Natural Language Processing Lab. Size • They generated five size graded expressions for each word. • For example, the feminine feature “skirt” generated the terms: “tiny skirt,” “small skirt,” “average skirt,” “big skirt,” “huge skirt.”

  14. NTU Natural Language Processing Lab.

  15. NTU Natural Language Processing Lab. Socialness • The results of the experiment found • relative3 (aunty, sibling, and groom) saw an average orientation of 0.16, thus leaning toward the feminine; • socialgroup1 (staff, church, and bikers) saw an average orientation of -0.22, thus leaning toward the masculine.

  16. NTU Natural Language Processing Lab.

  17. NTU Natural Language Processing Lab. Affect • ANEW – a set of normative affective ratings for 1034 common English words. • ANEW rates words using the pleasure-arousal-dominance (PAD) model of emotion. • PAD model • (P)leasure ranges from joy (+P) to reluctance (-P) • (A)rousal ranges from mental awareness (+A) to sleepiness (-A) • (D)ominance describes the agent‘s feelings of control over the situation

  18. NTU Natural Language Processing Lab. • Pleasuremale = 0.047; Pleasurefemale = 0.096 • Arousalmale = 0.048; Arousalfemale = 0.014 • They opted for Ekman’s ontology of six universal emotions + ’neutral. • Surprise, disgusted, fearful, angry, sad, happy + neutral

  19. NTU Natural Language Processing Lab.

  20. NTU Natural Language Processing Lab. Gender Lens • GENDERLENS • reading the news feed from a major news aggregator (Google News). • a news filtering system that reranks the daily news based on the gender biases learned from the blog data set.

  21. NTU Natural Language Processing Lab.

  22. NTU Natural Language Processing Lab. Evaluation • 30 users (15 men and 15 women)

More Related