Voltron: A Hybrid System For Answer Validation Based On Lexical And Distance Features

Abstract

The purpose of this paper is to describe our submission to the SemEval-2015 Task 3 on Answer Selection in Community Question Answering. We participated in subtask A, where the systems had to classify community answers for a given question as definitely relevant, potentially useful, or irrelevant. For every question-answer pair in the training data we extract a vector with a variety of features. These vectors are then fed to a MaxEnt classifier for training. Given a question and an answer the trained classifier outputs class probabilities for each of the three desired categories. The one with the highest probability is chosen. Our system scores better than the average score in subtask A of Task 3.

SUper Team at SemEval-2016 Task 3: Building a Feature-Rich System for Community Question Answering

“…We build our system on top of the framework developed by our colleagues (Zamanov et al, 2015). In particular, we approach the task as a classification problem similarly to the approach we took for Se-mEval 2015 Task 3 .…”

“…We build our preprocessing and feature extraction pipeline based on the system of Zamanov et al (2015), which was developed by a subset of our 2016 team for SemEval-2015 Task 3 on Answer Selection in Community Question Answering . The task in 2015 was to classify comments in a thread as relevant, potentially useful, or bad with respect to the thread question.…”

Enhanced answer selection in CQA using multi-dimensional features combination

“…We compared our method against the JAIST [21] , HITSZ-ICRC [22] , QCRI [23] , ECNU [24] , ICRC-HIT [25] , VectorSlu [26] , Shiraz [27] , FBK-HLT [28] , Voltron [29] , and CICBUAPnlp [30] .…”