A Shallow Approach for Answer Selection based on Dependency Trees and Term Density.
Manuel Pérez-Coutiño, Manuel Montes-y-Gómez, Aurelio López‐López, Luis Villaseñor-Pineda, Aarón Pancardo-Rodríguez · CLEF (Working Notes) · 2006
This paper describes the experiments performed for the QA@CLEF-2006 within the joint participation of the eLing Division at VEng and the Language Technologies Laboratory at INAOE. This year our laboratories have participated in the Spanish monolingual task, continue with their previous work described in [Perez-Coutino et al., 2005]. The aim of these experiments was to observe and quantify the possible improvement at the final step of the Question Answering prototype when some syntactic features were taken into the decision process. In order to reach this goal, a shallow approach to answer ranking based on the term density measure has been included. This measure weighs the number of question terms which have a syntactic dependency to one candidate answer within a relevant passage to the given question. Once the term density has been computed for each candidate answer, their weights along to the weights gathered in the previous steps are merged by a lineal combination to gather the final weight for each candidate answer. Finally, the answer selection process arranges candidate answers based on their weights, selecting the top-n as the Question Answering system answers. The approach described has shown a small but interesting improvement against the same Question Answering prototype without this module. Nevertheless, there are many variables to consider for a substantial improvement of the whole Question Answering system, and particularly at the initial steps, where passage retrieval and candidate answer selection are determinant for the improvement of system’s recall.