CMU LiveMedQA at TREC 2017 LiveQA: A Consumer Health Question Answering System
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Answer GenerationGeneral ClassificationQuestion AnsweringRetrievaltext-classificationText ClassificationSimilar Papers 제목 키워드 기반
Spelling Correction in Healthcare Query-Answer Systems: Methods, Retrieval Impact, and Empirical Evaluation
Healthcare question-answering (QA) systems face a persistent challenge: users submit queries with spelling errors at rates substantially higher than those found in the professional documents they search. This paper prese…
LiveQA: A Question Answering Dataset over Sports Live
In this paper, we introduce LiveQA, a new question answering dataset constructed from play-by-play live broadcast. It contains 117k multiple-choice questions written by human commentators for over 1,670 NBA games, which …
Multiple-choiceQuestion AnsweringIntegrating UMLS Knowledge into Large Language Models for Medical Question Answering
Large language models (LLMs) have demonstrated powerful text generation capabilities, bringing unprecedented innovation to the healthcare field. While LLMs hold immense promise for applications in healthcare, applying th…
Medical Question AnsweringQuestion AnsweringText GenerationA Question-Entailment Approach to Question Answering
One of the challenges in large-scale information retrieval (IR) is to develop fine-grained and domain-specific methods to answer natural language questions. Despite the availability of numerous sources and datasets for a…
Information RetrievalQuestion AnsweringQuestion SimilarityRetrievalCHQ-Summ: A Dataset for Consumer Healthcare Question Summarization
The quest for seeking health information has swamped the web with consumers' health-related questions. Generally, consumers use overly descriptive and peripheral information to express their medical condition or other he…
Community Question AnsweringDescriptiveNatural Language UnderstandingQuestion Answering