Generalization of parse trees for iterative taxonomy learning

Taxonomy and paragraph-level syntactic generalization are applied to relevance improvement in search and text similarity assessment. We conduct an evaluation of the search relevance improvement in vertical and horizontal domains and observe significant contribution of the learned taxonomy in the former, and a noticeable contribution of a hybrid system in the latter domain. We also perform industrial evaluation of taxonomy and syntactic generalization-based text relevance assessment and conclude that proposed algorithm for automated taxonomy learning is suitable for integration into industrial systems. Proposed algorithm is implemented as a part of Apache OpenNLP.Similarity project.