Algorithms, Experimentation

In this paper, we extensively study the use of syntactic and semantic structures obtained with shallow and deeper syntactic parsers in the answer passage reranking task. We propose several dependency-based structures enriched with Linked Open Data (LD) knowledge for representing pairs of questions and answer passages. We use such tree structures in learning to rank (L2R) algorithms based on tree kernel. The latter can represent questions and passages in a tree fragment space, where each substructure represents a powerful syn-tactic/semantic feature. Additionally since we define links between structures, tree kernels also generate relational features spanning question and passage structures. We derive very important find-ings, which can be useful to build state-of-the-art systems: (i) full syntactic dependencies can outperform shallow models also using external knowledge and (ii) the semantic information should be de-rived by effective and high-coverage resources, e.g., LD, and in-corporated in syntactic structures to be effective. We demonstrate our findings by carrying out an extensive comparative experimenta-tion on two different TREC QA corpora and one community ques-tion answer dataset, namely Answerbag. Our comparative analysis on well-defined answer selection benchmarks consistently demon-strates that our structural semantic models largely outperform the state of the art in passage reranking