Difference between revisions of "Eisenstein et al ACL 2011. Discovering Sociolinguistic Associations with Structured Sparsity"
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== Summary == | == Summary == | ||
− | This [[Category::Paper]] demonstrates how | + | 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. |
Revision as of 19:45, 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
Summary
This Paper demonstrates how regression with structured sparsity can be applied to select words and [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.