In this paper, we describe our experimentations in answer formulation for question-answering (QA) systems. In the context of QA, answer formulation can serve two purposes: improving answer extraction or improving human-computer interaction (HCI). Each purpose has different precision/recall requirements. We present our experiments for both purposes and argue that formulations of better linguisti...

The German question answering (QA) system IRSAW (formerly: InSicht) participated in QA@CLEF for the fifth time. IRSAW was introduced in 2007 by integrating the deep answer producer InSicht, several shallow answer producers, and a logical validator. InSicht builds on a deep QA approach: it transforms documents to semantic representations using a parser, draws inferences on semantic representatio...

What is the importance of cooperativity? That depends on the system, but let’s take hemoglobin. Hemoglobin’s mission is to pick up a large amount of oxygen in the lungs, where the oxygen concentration (or partial pressure) is about 100 torr, and then drop off a good fraction of it in the peripheral tissues where the oxygen concentration is about 20 torr. Cooperativity helps make this transport ...

Each day, millions of people ask questions and search for answers on the World Wide Web. Due to this, the Internet has grown to a world wide database of questions and answers, accessible to almost everyone. Since this database is so huge, it is hard to find out whether a question has been answered or even asked before. As a consequence, users are asking the same questions again and again, produ...

Research in Question Answering (QA) has been dominated by the TREC methodology of black-box system evaluation. This makes it difficult to evaluate the effectiveness of individual components and requires human involvement. We have collected a set of answer locations within the AQUAINT corpus for a sample of TREC questions, in doing so we also analyse the ability of humans to retrieve answers. Ou...

Community-driven Question Answering (CQA) systems that crowdsource experiential information in the form of questions and answers and have accumulated valuable reusable knowledge. Clustering of QA datasets from CQA systems provides a means of organizing the content to ease tasks such as manual curation and tagging. In this paper, we present a clustering method that exploits the two-part question...

I am in the middle of Manhattan and I am lost. I approach a likely stranger on the road and ask him, pointing to the right: “Is this the way to the railway station?” Discounting for a moment the possibility that I will be ignored and that the stranger will continue on his way (of which the—trivial—game move equivalent would be “I prefer not to answer the question”), I will now get one of three ...

Fine, so what does a population geneticist mean by epistasis? RA Fisher used ‘epistacy’ and later ‘epistasis’ to describe genetic interactions more generally [2]. We think that population geneticists hijacked this term over a decade after its coinage just to confuse the classical geneticists. OK, what does a medical doctor mean by epistasis? A thin film on the surface of a urine specimen. Enoug...

We introduce Question-Answer Meaning Representations (QAMRs), which represent the predicate-argument structure of a sentence as a set of question-answer pairs. We also develop a crowdsourcing scheme to show that QAMRs can be labeled with very little training, and gather a dataset with over 5,000 sentences and 100,000 questions. A detailed qualitative analysis demonstrates that the crowd-generat...