Language Challenges for Data Fusion in Question-Answering

AbstractSearch engines on the web and most existing question-answering systems provide the user with a set of hyperlinks and/or web page extracts containing answer(s) to a question. These answers are often incoherent to a certain degree (equivalent, contradictory, etc.). It is then quite difficult for the user to know which answer is the correct one. In this paper, we present an approach which aims at providing synthetic numerical answers in a question-answering system. These answers are generated in natural language and, in a cooperative perspective, the aim is to explain to the user the variation of numerical values when several values, apparently incoherent, are extracted from the web as possible answers to a question. We present in particular how lexical resources are essential to answer extraction from the web, to the characterization of the variation mode associated with the type of information and to answer generation in natural language.