Non-projective dependency parsing using spanning tree algorithms
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Learning to rank typed graph walks: local and global approaches
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Video suggestion and discovery for youtube: taking random walks through the view graph
Proceedings of the 17th international conference on World Wide Web
Weakly-supervised acquisition of labeled class instances using graph random walks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
User demographics and language in an implicit social network
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
| Hi-index | 0.00 |
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.