# UNCC Biomedical Semantic Question Answering Systems. BioASQ: Task-7B, Phase-B

Source: [UNCC Biomedical Semantic Question Answering Systems. BioASQ: Task-7B, Phase-B](<https://www.sota2.com/research/paper/68fa66fe1e209131cea13899>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:03:19.943Z

Authors: Sai Krishna Telukuntla, Aditya Kapri, Wlodek Zadrozny

Published: 2020-02-05T20:43:14.000Z

## Abstract

In this paper, we detail our submission to the 2019, 7th year, BioASQ competition. We present our approach for Task-7b, Phase B, Exact Answering Task. These Question Answering (QA) tasks include Factoid, Yes/No, List Type Question answering. Our system is based on a contextual word embedding model. We have used a Bidirectional Encoder Representations from Transformers(BERT) based system, fined tuned for biomedical question answering task using BioBERT. In the third test batch set, our system achieved the highest MRR score for Factoid Question Answering task. Also, for List type question answering task our system achieved the highest recall score in the fourth test batch set. Along with our detailed approach, we present the results for our submissions, and also highlight identified downsides for our current approach and ways to improve them in our future experiments.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2002.01984>)

[PDF](<https://arxiv.org/pdf/2002.01984.pdf>)