The UniDive 2025 Morphosyntactic Parsing (MSP) shared task introduces a representation unifying dependency structure, morphological features, and unrealized arguments.Unlike Universal Dependencies, MSP encodes abstract nodes (e.g., dropped subjects, implicit pronouns) as labels projected onto content words, which standard UD parsers cannot model.To address this challenge, in this paper we present a multilingual, typology-aware joint system integrating word-type prediction, content-only parsing, morphological tagging, and an abstract-node component within a single architecture.The model combines the baseline joint framework with typology-conditioned adapters and progressive weighting for abstract supervision.On the MSP test set, our model outperforms the leading submission by 3.23 percentage points in MSLAS, 3.35 in LAS, and 1.78 in FEATS macro F1, demonstrating the effectiveness of typology-sensitive multi-task learning in MSP.

The COVID-19 pandemic has affected the lives and health of many people. Among them, health care workers indeed played the biggest role. In this work, we investigate the health care workers job applications, and the job postings change with the time series analysis perspective. The dataset is provided by Kariyer.net, which is the foremost job-searching company in Turkey. Since the data set includes job applications over every region of country, the results reveal important facts about health care workers in Turkey. We aim to study the possible effect of vaccination on health care workers job search decisions.