Efficient kernels for sentence pair classification

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.

[1]  Alessandro Moschitti,et al.  Automatic Learning of Textual Entailments with Cross-Pair Similarities , 2006, ACL.

[2]  Alessandro Moschitti,et al.  Fast and effective kernels for relational learning from texts , 2007, ICML '07.

[3]  Jun Suzuki,et al.  Hierarchical Directed Acyclic Graph Kernel: Methods for Structured Natural Language Data , 2003, ACL.

[4]  Andrew Hickl,et al.  Recognizing Textual Entailment with LCC’s G ROUNDHOG System , 2005 .

[5]  J. Köbler,et al.  The Graph Isomorphism Problem: Its Structural Complexity , 1993 .

[6]  Roy Bar-Haim,et al.  The Second PASCAL Recognising Textual Entailment Challenge , 2006 .

[7]  Jan Ramon,et al.  Expressivity versus efficiency of graph kernels , 2003 .

[8]  Peter Clark,et al.  The Seventh PASCAL Recognizing Textual Entailment Challenge , 2011, TAC.

[9]  Ido Dagan,et al.  The Third PASCAL Recognizing Textual Entailment Challenge , 2007, ACL-PASCAL@ACL.

[10]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[11]  Günter Neumann,et al.  Recognizing Textual Entailment Using a Subsequence Kernel Method , 2007, AAAI.

[12]  Bob Carpenter,et al.  The logic of typed feature structures , 1992 .

[13]  Alessandro Moschitti,et al.  A Study on Convolution Kernels for Shallow Statistic Parsing , 2004, ACL.

[14]  Christopher D. Manning,et al.  Robust Textual Inference using Diverse Knowledge Sources , 2005 .

[15]  Andrew Y. Ng,et al.  Robust Textual Inference via Graph Matching , 2005, HLT.

[16]  Jason Eisner,et al.  Learning Non-Isomorphic Tree Mappings for Machine Translation , 2003, ACL.

[17]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[18]  Jie Wang,et al.  Average-case computational complexity theory , 1998 .

[19]  Michael Collins,et al.  New Ranking Algorithms for Parsing and Tagging: Kernels over Discrete Structures, and the Voted Perceptron , 2002, ACL.

[20]  Thomas Gärtner,et al.  A survey of kernels for structured data , 2003, SKDD.

[21]  Ido Dagan,et al.  PROBABILISTIC TEXTUAL ENTAILMENT: GENERIC APPLIED MODELING OF LANGUAGE VARIABILITY , 2004 .

[23]  Christopher D. Manning,et al.  Learning to distinguish valid textual entailments , 2006 .