L2 learners often produce "ungrammatical" word combinations such as, e.g., *give a suggestion or *make a walk. This is because of the "collocationality" of one of their items (the base) that limits the acceptance of collocates to express a specific meaning ('perform' above). We propose an algorithm that delivers, for a given base and the intended meaning of a collocate, the actual collocate lexeme(s) (make / take above). The algorithm exploits the linear mapping between bases and collocates from examples and generates a collocation transformation matrix which is then applied to novel unseen cases. The evaluation shows a promising line of research in collocation discovery.

The main purpose of this study was to examine the relationship between resilience and health-related quality of life in patients following COVID-19 disease among those with and without lingering symptoms. The study design is descriptive and cross-sectional. Participants ( n = 97) were adults who had earlier contracted COVID-19 disease and were in post-infection status between July and October 2020. Participants completed the following instruments: Connor-Davidson Resilience Scale, Short-Form 12-item Health Survey, and Hospital Anxiety and Depression Scale. Approximately 35% post-COVID-19 patients had a low level of resilience. The impact on the health status and resilience of those who had reported symptoms after 6 months was also significant. Age and depression had a significant impact on level of resilience. This relationship can affect patient recovery and negatively impact the ability to cope with COVID-19 disease. It is necessary to implement specialized training for clinicians on the effects of long-term COVID-19 to improve patient care. Long COVID symptoms might manifest months after an acute COVID-19 illness; clinicians who can confirm patient reports of these symptoms may help patients recover and become more resilient.