Levels of Reasons Why and Answers to Why Questions

Learning to Rank Answers to Why-Questions

The goal of the current research project is to develop a ques­ tion answering system for answering why-questions (why QA). Our system is a pipeline consisting of an off-the-shelf retrieval module followed by an answer re-ranking module. In this paper, we aim at improving the ranking performance of our system by finding the optimal approach to learning to rank. More specifically, we try to find ...

Discourse-based answering of why-questions Employing RST structure for finding answers to why-questions

This paper presents the work that we have carried out in investigating the purpose of discourse structure for why-question answering (why-QA). We developed a system for answering why-questions that employs the discourse relations in a pre-annotated document collection (the RST Treebank). With this method, we obtain a recall of 53.3% with a mean reciprocal rank (MRR) of 0.662. We argue that the ...

Why do you ask? Good questions provoke informative answers

What makes a question useful? What makes an answer appropriate? In this paper, we formulate a family of increasingly sophisticated models of question-answer behavior within the Rational Speech Act framework. We compare these models based on three different pieces of evidence: first, we demonstrate how our answerer models capture a classic effect in psycholinguistics showing that an answerer’s l...

Why is Helicobacter pylori disappearing? More questions than answers.

Learning to Rank QA Data Evaluating Machine Learning Techniques for Ranking Answers to Why-Questions

In this work, we evaluate a number of machine learning techniques for the purpose of ranking answers to why-questions. We use a set of 37 linguistically motivated features that characterize questions and answers. We experiment with a number of machine learning techniques in various settings. The purpose of the experiments is to assess how the different machine learning techniques can cope with ...