Author:
NASTASE VIVI,MIHALCEA RADA,RADEV DRAGOMIR R.
Abstract
AbstractGraphs are a powerful representation formalism that can be applied to a variety of aspects related to language processing. We provide an overview of how Natural Language Processing problems have been projected into the graph framework, focusing in particular on graph construction – a crucial step in modeling the data to emphasize the phenomena targeted.
Publisher
Cambridge University Press (CUP)
Subject
Artificial Intelligence,Linguistics and Language,Language and Linguistics,Software
Reference136 articles.
1. Zhu X. , and Lafferty J. 2005. Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning. In Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany, 7–11 August 2005, pp. 1052–1059.
2. Zhu X. 2007. Semi-supervised learning literature survey. Technical Report TR 1530, Computer Sciences, University of Wisconsin, Madison.
3. Yan R. , Lapata M. , and Li X. 2012. Tweet recommendation with graph co-ranking. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, Jeju Island, Korea, 8–14 July 2012, pp. 516–525.
4. Widdows D. , and Dorow B. 2002. A graph model for unsupervised lexical acquisition. In Proceedings of the 19th International Conference on Computational Linguistics, Taipei, Taiwan, 24 August–1 September 2002.
Cited by
57 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献