A Semi-supervised learning approach to enhance health care Community-based Question Answering: A case study in alcoholism

TL;DR

This paper presents a semi-supervised learning algorithm to automatically answer health-related questions on community Q&A sites, improving response accuracy by leveraging historical data and health-specific features.

Contribution

The study introduces a semi-supervised learning approach that enhances automatic question answering in health communities using health-related features and information retrieval techniques.

Findings

Achieved 86.2% accuracy on Yahoo! Answers dataset.

UMLS-based features improved performance by approximately 8%.

Key features include text length, stop words, question similarity, and health term overlap.

Abstract

Community-based Question Answering (CQA) sites play an important role in addressing health information needs. However, a significant number of posted questions remain unanswered. Automatically answering the posted questions can provide a useful source of information for online health communities. In this study, we developed an algorithm to automatically answer health-related questions based on past questions and answers (QA). We also aimed to understand information embedded within online health content that are good features in identifying valid answers. Our proposed algorithm uses information retrieval techniques to identify candidate answers from resolved QA. In order to rank these candidates, we implemented a semi-supervised leaning algorithm that extracts the best answer to a question. We assessed this approach on a curated corpus from Yahoo! Answers and compared against a rule-based string similarity baseline. On our dataset, the semi-supervised learning algorithm has an accuracy of 86.2%. UMLS-based (health-related) features used in the model enhance the algorithm's performance by proximately 8 %. A reasonably high rate of accuracy is obtained given that the data is considerably noisy. Important features distinguishing a valid answer from an invalid answer include text length, number of stop words contained in a test question, a distance between the test question and other questions in the corpus as well as a number of overlapping health-related terms between questions. Overall, our automated QA system based on historical QA pairs is shown to be effective according to the data set in this case study. It is developed for general use in the health care domain which can also be applied to other CQA sites.