SUper Team at SemEval-2016 Task 3: Building a feature-rich system for community question answering
Not on the map. Why?
Abstract (arXiv)
We present the system we built for participating in SemEval-2016 Task 3 on Community Question Answering. We achieved the best results on subtask C, and strong results on subtasks A and B, by combining a rich set of various types of features: semantic, lexical, metadata, and user-related. The most important group turned out to be the metadata for the question and for the comment, semantic vectors trained on QatarLiving data and similarities between the question and the comment for subtasks A and C, and between the original and the related question for Subtask B.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 36 references that are in the map. First posted on arXiv 26 Sep 2021.
Builds on
7 of 36 references.
References · 7
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
- 2019Pairwise Neural Machine Translation Evaluationcited by 11
- 2019SemEval-2015 Task 3: Answer Selection in Community Question Answeringcited by 10
- 2019Hunting for Troll Comments in News Community Forumscited by 8
- 2019Global Thread-Level Inference for Comment Classification in Community Question Answeringcited by 5
- 2019SemanticZ at SemEval-2016 Task 3: Ranking Relevant Answers in Community Question Answering Using Semantic Similarity Based on Fine-tuned Word Embeddingscited by 2
- 2019Machine Translation Evaluation Meets Community Question Answeringcited by 1
Built on by
Not cited yet.