We present a lightweight, feature-based regression system for predicting \textbf{valence} (pleasantness) and \textbf{arousal} (activation) from longitudinal language data. The language data ranges from longer free-form ecological essays to short affect-word, organized by user and time, reflecting natural variation in affective expression and experience. Our approach combines three complementary signals: (i) sentence-level semantic embeddings, (ii) psycholinguistic category features capturing affect- and function-related word usage, (iii) similarity measures between the language data with archetypal sentences, and (iv) trainable user-embeddings to account for between-user differences. The resulting feature vector is passed to a multi-layer perceptron trained to jointly predict valence and arousal. Our design provides a strong and interpretable baseline by making it possible to isolate the contribution of semantic, psycholinguistic, similarity, and user-specific signals. We further analyze our model’s predictions to identify which feature groups are most informative and where errors are concentrated across users and input types.

Automated knowledge graph (KG) construction involves two core components: nested named entity recognition (NER) and relation extraction (RE). In this work, we address cross-domain nested NER alongside RE. We integrate domain adaptation methods within a mixture-of-experts ensemble system for nested NER from Arabic text, evaluated in a cross-domain setting. The system combines multiple Arabic pretrained encoders for token- and span-level classification with domain data augmentation, and integrates their predictions using a vote-based ensemble routing with a complementary XLM-R expert. For RE, we employ an adversarially trained relation classifier backed by auxiliary entity types, NLI reranking, and structural schema rules, treating no_relation as a standard relation label. We additionally study a positive-relation setting that excludes no_relation from both training and inference. Our NER system reaches 0.7438 micro F1 on the hidden test set. For RE, the positive-relation setting achieves 0.9608 micro F1, while the no_relation-aware system achieves 0.9295.