Typed graph models for semi-supervised learning of name ethnicity
HLT '11: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2(2011)
摘要
This paper presents an original approach to semi-supervised learning of personal name ethnicity from typed graphs of morphophonemic features and first/last-name co-occurrence statistics. We frame this as a general solution to an inference problem over typed graphs where the edges represent labeled relations between features that are parameterized by the edge types. We propose a framework for parameter estimation on different constructions of typed graphs for this problem using a gradient-free optimization method based on grid search. Results on both in-domain and out-of-domain data show significant gains over 30% accuracy improvement using the techniques presented in the paper.
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关键词
inference problem,accuracy improvement,different construction,edge type,general solution,gradient-free optimization method,grid search,last-name co-occurrence statistic,morphophonemic feature,original approach,Typed graph model,name ethnicity,semi-supervised learning
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