Abstract
In this paper, we propose a novel class of graphs, the tripartite directed acyclic graphs (tDAGs), to model first-order rule feature spaces for sentence pair classification. We introduce a novel algorithm for computing the similarity in first-order rewrite rule feature spaces. Our algorithm is extremely efficient and, as it computes the similarity of instances that can be represented in explicit feature spaces, it is a valid kernel function. © 2009 ACL and AFNLP.
Cite
CITATION STYLE
Zanzotto, F. M., & Dell’Arciprete, L. (2009). Efficient kernels for sentence pair classification. In EMNLP 2009 - Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: A Meeting of SIGDAT, a Special Interest Group of ACL, Held in Conjunction with ACL-IJCNLP 2009 (pp. 91–100). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1699510.1699523
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