E97-1030 to the hierarchical tag context tree construction . As illustrated in Figure 3
H01-1072 SIMILARITY-BASED TREE CONSTRUC - TION The tree construction algorithm is based on the machine
H01-1072 labor between the chunking and tree construction modules can best be illustrated
H01-1072 tree construction level . The tree construction is similar to the DOP approach
H01-1072 parses . The algorithm used for tree construction is presented in a slightly simplified
C86-1076 compare the examples of two parse tree constructions ( Fig I ) : VP VP . / \ , / \
D11-1064 model in Eq . 4 . 2.1 Decision Tree Construction We use recursive partitioning
D09-1053 injected at different levels of tree construction . We found that the most effective
C96-2158 interdependency between parse tree construction and anaphor resolution . Up to
H01-1072 function-argument structure . T " uSBL 's tree construction algorithm relies on techniques
C94-2210 generalisations in the decision tree construction process ) . With the use of syllables
H01-1072 remodeled into complete trees in the tree construction level . The tree construction
H01-1072 flat struc - ture . T " uSBL 's tree construction module enriches the chunk output
D11-1064 randomizations without degrading the tree construction algorithm . That is , we use
D09-1116 − n +1 ) ) ( 4 ) While the tree construction algorithm is fairly standard
E06-1038 subtrees from x into possibly new tree constructions ) or drops ( drops words and
D08-1009 of the textual corpus , proof tree construction over PF relations also scales
D13-1159 existing entity repository . 2 . Tree construction . we build the co-occurrence
D13-1159 extraction ( Section 3.2.1 ) , tree construction ( Section 3.2.2 ) , and hierarchical
D11-1064 Section 2 . Each set was com - tree construction without apparent degradation
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