UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

ACQUA: Automated Community-based Question Answering through the Discretisation of Shallow Linguistic Features

Gkotsis, George; (2015) ACQUA: Automated Community-based Question Answering through the Discretisation of Shallow Linguistic Features. Journal of Web Science , 1 (1) pp. 1-15. 10.1561/106.00000001. Green open access

[thumbnail of technik,+106.00000001.pdf]
Preview
Text
technik,+106.00000001.pdf - Published Version

Download (1MB) | Preview

Abstract

This paper addresses the problem of determining the best answer in Community-based Question Answering (CQA) websites by focussing on the content. In particular, we present a novel system, ACQUA (http://acqua.kmi.open. ac.uk), that can be installed onto the majority of browsers as a plugin. The service offers a seamless and accurate prediction of the answer to be accepted. Our system is based on a novel approach for processing answers in CQAs. Previous research on this topic relies on the exploitation of community feedback on the answers, which involves rating of either users (e.g., reputation) or answers (e.g. scores manually assigned to answers). We propose a new technique that leverages the content/textual features of answers in a novel way. Our approach delivers better results than related linguistics-based solutions and manages to match rating-based approaches. More specifically, the gain in performance is achieved by rendering the values of these features into a discretised form. We also show how our technique manages to deliver equally good results in real-time settings, as opposed to having to rely on information not always readily available, such as user ratings and answer scores. We ran an evaluation on 21 StackExchange websites covering around 4 million questions and more than 8 million answers. We obtain 84% average precision and 70% recall, which shows that our technique is robust, effective, and widely applicable.

Type: Article
Title: ACQUA: Automated Community-based Question Answering through the Discretisation of Shallow Linguistic Features
Open access status: An open access version is available from UCL Discovery
DOI: 10.1561/106.00000001
Publisher version: http://dx.doi.org/10.1561/106.00000001
Language: English
Additional information: This is published under the terms of CC BY-NC-ND 2.0.
Keywords: Community Question Answering, Social Media, Natural Language Processing
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of Arts and Humanities
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of Arts and Humanities > Dept of Information Studies
URI: https://discovery.ucl.ac.uk/id/eprint/10181065
Downloads since deposit
4Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item