# SemanticZ at SemEval-2016 Task 3: Ranking Relevant Answers in Community Question Answering Using Semantic Similarity Based on Fine-tuned Word Embeddings

arXiv: https://arxiv.org/abs/1911.08743 · HTML: https://mlsanity.com/paper/1911.08743/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [PerCQA: Persian Community Question Answering Dataset](https://mlsanity.com/paper/2112.13238/) (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)

We describe our system for finding good answers in a community forum, as defined in SemEval-2016, Task 3 on Community Question Answering. Our approach relies on several semantic similarity features based on fine-tuned word embeddings and topics similarities. In the main Subtask C, our primary submission was ranked third, with a MAP of 51.68 and accuracy of 69.94. In Subtask A, our primary submission was also third, with MAP of 77.58 and accuracy of 73.39.