Аннотации:
As the as the volume of user-generated content in social media expands so do the potential benefits of mining social media to learn about patient conditions, drug indications, and beneficial or adverse drug reactions. In this paper, we apply Conditional Random Fields (CRF) model for extracting expressions related to diseases from patient comments. Our method utilizes hand-crafted features including contextual features, dictionaries, clusterbased and distributed word representation generated from unlabeled user posts in social media. We compare our CRF-based approach with deep recurrent neural networks and a dictionary-based approach. We examine different word embeddings generated from unlabeled user posts in social media and scientific literature. We show that CRF outperformed other methods and achieved the F1-measures of 69.1% and 79.4% on recognition of disease-related expressions in the exact and partial matching exercises, respectively. Qualitative evaluation of disease-related expressions recognized by our feature-rich CRF-based approach demonstrates the variability of reactions from patients with different health conditions.
