Input: A set of syntactic trees for sentences (S); the depth range (D) Output: A set of sub-tree patterns extracted from S 1: P atterns = {} 2: for all Syntactic tree T ∈ S do 3: Nodes ← Top-down level order traversal of T 4: for all node n ∈ Nodes do 5: Extract subtree p rooted under node n, with depth within the range D 6: p ← generalize(p) 7: P atterns.add(p) 8: end for 9: end for 10: return P atterns Although Conditional Random Fields (CRF) is good sequential learning algorithm and has been used in other related work (Cong et al., 2008), here we select Support Vector Machines (SVM) as an alternative learner.
the state-of-the-art retrieval models include the vector space model (Duan et al., 2008), language model (Duan et al., 2008;Jeon et al., 2005), Okapi model (Jeon et al., 2005), translation model (Jeon et al., 2005;Rie-zler et al., 2007;Xue et al., 2008), and syntactic tree matching model (Wang et al., 2009).