2017/11/15 by Yuan Yang, Yang Yuan, Yang, Yuan +8
Computer Science · #Computation and Language (cs.CL) #Expert finding and Q&A systems #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Multimodal Machine Learning Applications #Topic Modeling #cs.CL #cs.IR
paper · pdf · doi:10.48550/arxiv.1711.05789
To appear in Proceedings of TREC 2017
arxiv created 2017/11/15 · openalex publication_date 2017/11/15 · arxiv updated 2017/11/17 · openalex created_date 2017/12/04 · openalex updated_date 2026/07/28
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.