RelTextRank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair Representations

We present a highly-flexible UIMA-based pipeline for developing structural kernelbased systems for relational learning from text, i.e., for generating training and test data for ranking, classifying short text pairs or measuring similarity between pieces of text. For example, the proposed pipeline can represent an input question and answer sentence pairs as syntacticsemantic structures, enriching them with relational information, e.g., links between question class, focus and named entities, and serializes them as training and test files for the tree kernel-based reranking framework. The pipeline generates a number of dependency and shallow chunkbased representations shown to achieve competitive results in previous work. It also enables easy evaluation of the models thanks to cross-validation facilities.