Collaborative Learning for Answer Selection in Question Answering

Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering

In this paper, the answer selection problem in community question answering (CQA) is regarded as an answer sequence labeling task, and a novel approach is proposed based on the recurrent architecture for this problem. Our approach applies convolution neural networks (CNNs) to learning the joint representation of questionanswer pair firstly, and then uses the joint representation as input of the...

A Probabilistic Framework for Answer Selection in Question Answering

This paper describes a probabilistic answer selection framework for question answering. In contrast with previous work using individual resources such as ontologies and the Web to validate answer candidates, our work focuses on developing a unified framework that not only uses multiple resources for validating answer candidates, but also considers evidence of similarity among answer candidates ...

Answer Formulation for Question-Answering

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...

SVM Answer Selection for Open-Domain Question Answering

Predicting Quality of Answer in Collaborative Question Answer Learning

Studies over the years shown that students had actively and more interactively involved in a classroom discussion to gain their knowledge. By posting questions for other participants to answer, students could obtain several answers to their question. The problem is sometimes the answer chosen by student as the best answer is not necessarily the best quality answer. Therefore, an automatic recom...