Extracting sentiment elements using pre-trained generative models has recently led to large improvements in aspect-based sentiment analysis benchmarks. However, these models always need large-scale computing resources, and they also ignore explicit modeling of structure between sentiment elements. To address these challenges, we propose an opinion tree parsing model, aiming to parse all the sentiment elements from an opinion tree, which is much faster, and can explicitly reveal a more comprehensive and complete aspect-level sentiment structure. In particular, we first introduce a novel context-free opinion grammar to normalize the opinion tree structure. We then employ a neural chart-based opinion tree parser to fully explore the correlations among sentiment elements and parse them into an opinion tree structure. Extensive experiments show the superiority of our proposed model and the capacity of the opinion tree parser with the proposed context-free opinion grammar. More importantly, the results also prove that our model is much faster than previous models.

A precision medicine (PM) pipeline was developed to determine the optimal treatment regime for participants in an exercise (E), dietary weight loss (D), and D+E randomized clinical trial for knee osteoarthritis to maximize their expected outcomes. Using data from 343 participants of the Intensive Diet and Exercise for Arthritis (IDEA) trial, we applied 24 machine-learning models to develop individualized treatment rules on seven outcomes: SF-36 physical component score, weight loss, WOMAC pain/function/stiffness scores, compressive force, and IL-6. The optimal precision medicine model (PMM) was selected based on jackknife value function estimates that indicate improvement in the outcome(s) had future participants followed the estimated decision rule, which is then compared against the optimal single, fixed treatment model called zero-order model (ZOM) with a Z-test. Multiple outcome random forest was the optimal model for the WOMAC outcomes. The PMMs supported the overall findings from IDEA that the D+E intervention was optimal for most participants, but there was evidence that a subgroup of participants would likely benefit more from diet alone for two outcomes. This article provides detailed technical background for the clinical data analysis.