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Found 9 papers, 2 papers with code
Looking from the Inside: How Children Render Character’s Perspectives in Freely Told Fantasy Stories
Story characters not only perform actions, they typically also perceive, feel, think, and communicate.
UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentation
The goal of the task is to identify if a sentence is deemed acceptable or not, depending on the taxonomic relationship that holds between a noun pair contained in the sentence.
Bias Discovery in Machine Learning Models for Mental Health
We computed fairness metrics and present bias mitigation strategies using a model trained on clinical mental health data.
Federated learning for violence incident prediction in a simulated cross-institutional psychiatric setting
In this work, we investigate the application of Federated Learning to clinical Natural Language Processing, applied to the task of Violence Risk Assessment by simulating a cross-institutional psychiatric setting.
Making sense of violence risk predictions using clinical notes
Violence risk assessment in psychiatric institutions enables interventions to avoid violence incidents.
Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes
We explore conventional and deep machine learning methods to assess violence risk in psychiatric patients using practitioner notes.
Dutch Named Entity Recognition and De-identification Methods for the Human Resource Domain
The human resource (HR) domain contains various types of privacy-sensitive textual data, such as e-mail correspondence and performance appraisal.
UU\_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain
This article describes the participation of the UU{\_}TAILS team in the 2019 MEDIQA challenge intended to improve domain-specific models in medical and clinical NLP.
DEDUCE: A pattern matching method for automatic de-identification of Dutch medical text
In order to use medical text for research purposes, it is necessary to de-identify the text for legal and privacy reasons.
