Knowledge-Aware Neural Networks for Medical Forum Question Classification
Not on the map. Why?
Abstract (arXiv)
Online medical forums have become a predominant platform for answering health-related information needs of consumers. However, with a significant rise in the number of queries and the limited availability of experts, it is necessary to automatically classify medical queries based on a consumer's intention, so that these questions may be directed to the right set of medical experts. Here, we develop a novel medical knowledge-aware BERT-based model (MedBERT) that explicitly gives more weightage to medical concept-bearing words, and utilize domain-specific side information obtained from a popular medical knowledge base. We also contribute a multi-label dataset for the Medical Forum Question Classification (MFQC) task. MedBERT achieves state-of-the-art performance on two benchmark datasets and performs very well in low resource settings.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 33 references that are in the map. First posted on arXiv 27 Sep 2021.
Builds on
15 of 33 references.
References · 15
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2018Deep contextualized word representationscited by 2,285
- 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,935
- 2014Convolutional Neural Networks for Sentence Classificationcited by 1,124
- 2016Bag of Tricks for Efficient Text Classificationcited by 869
- 2018Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledgecited by 48
- 2019Leveraging Knowledge Bases in LSTMs for Improving Machine Readingcited by 37
- 2019A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extractioncited by 18
- 2018Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysiscited by 17
- 2021Fast and Effective Biomedical Entity Linking Using a Dual Encodercited by 16
Built on by
Not cited yet.