Rivindu Perera


Answer Presentation with Contextual Information: A Case Study using Syntactic and Semantic Models

Abstract
Answer presentation is a subtask in Question Answering that investigates the ways of presenting an acquired answer to the user in a format that is close to a human generated answer. In this research we explore models to retrieve additional, relevant, contextual information corresponding to a question and present an enriched answer by inte- grating the additional information as natural language. We investigate the role of Bag of Words (BoW) and Bag of Concepts (BoC) models to retrieve the relevant contextual information. The information source utilized to retrieve the information is a Linked Data resource, DBpedia, which encodes large amounts of knowledge corresponding to Wikipedia in a structured form as triples. The experiments utilizes the QALD ques- tion sets consisted of training and testing sets each containing 100 ques- tions. The results from these experiments shows that pragmatic aspects, which are often neglected by BoW (syntactic models) and BoC (semantic models), form a critical part of contextual information selection.