Difference between revisions of "Eisenstein et al ACL 2011. Discovering Sociolinguistic Associations with Structured Sparsity"

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== Summary ==
 
== Summary ==
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This [[Category::Paper]] demonstrates how regression with structured sparsity can be applied to select words and [http://en.wikipedia.org/wiki/Demography demographic] features that reveal sociolinguistic associations. Modelling sociolinguistic association is a complex problem because of the large number of possible interactions involved. Using multi-output regression with structured sparsity, this method identifies a small subset of words (lexical items, in general) that are most influenced by demographics and also discovers conjunction of demographic attributes that influence variation in lexical items.
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This [[Category::Paper]] studies the influence of [http://en.wikipedia.org/wiki/Demography demography] over language. In other words, it tries to identify the lexical variations with respect to certain demographic attributes (race or ethnicity, socioeconomic status, language spoken etc). Modelling sociolinguistic association is a complex problem because of the large number of possible interactions involved. Using multi-output regression with structured sparsity, this method identifies a small subset of words that are most influenced by demographics and also discovers conjunction of demographic attributes that influence variation in lexical items.

Revision as of 19:59, 30 September 2012

Citation

Jacob Eisenstein, Noah A. Smith and Eric P. Xing Discovering Sociolinguistic Associations with Structured Sparsity in Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL 2011), Portland

Online Version

Online Pdf

Summary

This Paper studies the influence of demography over language. In other words, it tries to identify the lexical variations with respect to certain demographic attributes (race or ethnicity, socioeconomic status, language spoken etc). Modelling sociolinguistic association is a complex problem because of the large number of possible interactions involved. Using multi-output regression with structured sparsity, this method identifies a small subset of words that are most influenced by demographics and also discovers conjunction of demographic attributes that influence variation in lexical items.

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