# Global Thread-Level Inference for Comment Classification in Community Question Answering

arXiv: https://arxiv.org/abs/1911.08755 · HTML: https://mlsanity.com/paper/1911.08755/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2019.

## Built on by (most cited first, 5 of 5)

- [Graph-Based Tri-Attention Network for Answer Ranking in CQA](https://mlsanity.com/paper/2103.03583/) (2021)
- [Exposing Paid Opinion Manipulation Trolls](https://mlsanity.com/paper/2109.13726/) (2021)
- [Enhancing Answer Selection in Community Question Answering with Pre-trained and Large Language Models](https://mlsanity.com/paper/2311.17502/) (2023)
- [Sentiment Analysis in Twitter for Macedonian](https://mlsanity.com/paper/2109.13725/) (2021)
- [SUper Team at SemEval-2016 Task 3: Building a feature-rich system for community question answering](https://mlsanity.com/paper/2109.15120/) (2021)

## Abstract (arXiv)

Community question answering, a recent evolution of question answering in the Web context, allows a user to quickly consult the opinion of a number of people on a particular topic, thus taking advantage of the wisdom of the crowd. Here we try to help the user by deciding automatically which answers are good and which are bad for a given question. In particular, we focus on exploiting the output structure at the thread level in order to make more consistent global decisions. More specifically, we exploit the relations between pairs of comments at any distance in the thread, which we incorporate in a graph-cut and in an ILP frameworks. We evaluated our approach on the benchmark dataset of SemEval-2015 Task 3. Results improved over the state of the art, confirming the importance of using thread level information.