CMU LiveMedQA at TREC 2017 LiveQA: A Consumer Health Question Answering System
This work addresses the challenge of providing accurate answers to consumer health queries, though it is incremental with identified drawbacks for future improvement.
The authors tackled the problem of answering consumer health questions by developing LiveMedQA, a system that achieved an average score of 0.356 on a 3-point scale in the TREC 2017 LiveQA medical subtask.
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.