Recognizing Textual Entailment by Soft Dependency Tree Matching

Rohini Basak, Sudip Kumar Naskar, Partha Pakray, Alexander Gelbukh


We present a rule-based method for recognizing entailment relation between a pair of text fragments by comparing their dependency tree structures. We used a dependency parser to generate the dependency triples of the text–hypothesis pairs. A dependency triple is an arc in the dependency parse tree. Each triple in the hypothesis is checked against all the triples in the text to find a matching pair. We have developed a number of matching rules after a detailed analysis of the PETE dataset, which we used for the experiments. A successful match satisfying any of these rules assigns a matching score of 1 to the child node of that particular arc in the hypothesis dependency tree. Then the dependency parse tree is traversed in post-order way to obtain the final entailment score at the root node. The scores of the leaf nodes are propagated from the bottom of the tree to the non-leaf nodes, up to the root node. The entailment score of the root node is compared against a predefined threshold value to make the entailment decision. Experimental results on the PETE dataset show an accuracy of 87.69% on the development set and 73.75% on the test set, which outperforms the state-of-the-art results reported on this dataset so far. We did not use any other NLP tools or knowledge sources, to emphasize the role of dependency parsing in recognizing textual entailment.


textual entailment, dependency parsing, dependency relation matching, rules, PETE dataset

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