Phenomena Explanation from Text: Unsupervised Learning of Interpretable and Statistically Significant Knowledge

Learning knowledge from text is becoming increasingly important as the amount of unstructured content on the Web rapidly grows. Despite recent breakthroughs in natural language understanding, the explanation of phenomena from textual documents is still a difficult and poorly addressed problem. Additionally, current NLP solutions often require labeled data, are domain-dependent, and based on black box models. In this paper, we introduce POIROT, a new descriptive text mining methodology for phenomena explanation from documents corpora. POIROT is designed to provide accurate and interpretable results in unsupervised settings, quantifying them based on their statistical significance. We evaluated POIROT on a medical case study, with the aim of learning the “voice of patients” from short social posts. Taking Esophageal Achalasia as a reference, we automatically derived scientific correlations with 79% F1-measure score and built useful explanations of the patients’ viewpoint on topics such as symptoms, treatments, drugs, and foods. We make the source code and experiment details publicly available (https://github.com/unibodatascience/POIROT).