# CMU LiveMedQA at TREC 2017 LiveQA: A Consumer Health Question Answering System

Source: [CMU LiveMedQA at TREC 2017 LiveQA: A Consumer Health Question Answering System](<https://www.sota2.com/research/paper/68fa63d41e209131cea010e9>)

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

Data updated: 2025-10-25T20:50:10.530Z

Authors: Yuan Yang, Jingcheng Yu, Ye Hu, Xiaoyao Xu, Eric Nyberg

Published: 2017-11-15T20:26:42.000Z

## Abstract

In this paper, we present LiveMedQA, a question answering system that is optimized for consumer health question. On top of the general QA system pipeline, we introduce several new features that aim to exploit domain-specific knowledge and entity structures for better performance. This includes a question type/focus analyzer based on deep text classification model, a tree-based knowledge graph for answer generation and a complementary structure-aware searcher for answer retrieval. LiveMedQA system is evaluated in the TREC 2017 LiveQA medical subtask, where it received an average score of 0.356 on a 3 point scale. Evaluation results revealed 3 substantial drawbacks in current LiveMedQA system, based on which we provide a detailed discussion and propose a few solutions that constitute the main focus of our subsequent work.

## 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/1711.05789>)

[PDF](<https://arxiv.org/pdf/1711.05789.pdf>)