ASURA: A Best-Answer Estimation System for NTCIR-8 CQA Pilot Task

Best-Answer Selection Using a Machine Learning Tool at NTCIR8 CQA Pilot Task

Overview of the NTCIR-8 Community QA Pilot Task (Part I): The Test Collection and the Task

Identifying high-quality content in community-type Q&A (CQA) sites is important. We propose a task in which a computer identifies good answers from such sites. We describe the design of our bestanswer estimation task using Yahoo! Chiebukuro. We also describe a method of constructing the test collection used for our CQA pilot task, the manual assessment method, and assessment results.

Microsoft Research Asia with Redmond at the NTCIR-8 Community QA Pilot Task

In this paper, we describe our approaches that we used for the NTCIR-8 Community QA Pilot task and report on its results. Specifically in the pilot task, we mainly focused on discovering effective features for evaluating quality of answers, for example, features on relevance of an answer to a question, authority of an answerer, or informativeness of an answer. Also, we examined two different st...

Overview of the Eighth NTCIR Workshop

For the Eighth NTCIR Workshop (NTCIR-8), we selected and organized seven research areas as “tasks” to investigate, test and benchmark newly constructed test collections. These areas are Complex Cross-Lingual Question Answering (CCLQA), Information Retrieval for Question Answering (IR4QA), Geographic and Temporal Search (GeoTime), Multilingual Opinion Analysis (MOAT), Patent Machine Translation ...

Incorporate Credibility into Context for the Best Social Media Answers

In this paper, we focus on the task of identifying the best answer for a usergenerated question in Collaborative Question Answering (CQA) services. Given that most existing research on CQA has focused on non-textual features such as click-through counts which are relatively difficult to access, we examine the effectiveness of diverse content-based features for the task. Specially, we propose to...