Automatic Extraction of Subcategorization Frames for Czech
Anoop Sarkar, Daniel Zeman
Abstract
We present some novel machine learning techniques for the identification of subcategorization information for verbs in Czech. We compare three different statistical techniques applied to this problem. We show how the learning algorithm can be used to discover previously unknown subcategorization frames from the Czech Prague Dependency Treebank. The algorithm can then be used to label dependents of a verb in the Czech treebank as either arguments or adjuncts. Using our techniques, we ar able to achieve 88% precision on unseen parsed text.
Create a lesson
Related papers
RetireOPD: Self-Retiring On-Policy Distillation for Agentic Reinforcement Learning
Yan Yu, Zhengxi Lu, Yizhou Liu et al.
Harm Laundering in GPT Models: Evidence That Gender Discrimination Is Transformed Rather Than Reduced Across Safety-Trained Generations
Sarah Wyer, Sue Black, Noura Al Moubayed
dQwen3.5: Hybrid-Attention Diffusion Language Models
Anton Xue, Litu Rout, Aditya Akella et al.
On-Demand Attention: Language Models Know When to Recall
Haibo Feng, Ruiqi Liang, Hanyang Peng et al.
Summarization Bias: The Directional Collapse of Objective Projection into Told-Mode Labels in Large Language Models --- A Conceptual Framework and Registered Test Protocol
Levent Bulut
HerHealthEval: Evaluating Multilingual and Register-Sensitive Understanding of Women's Health Communication
Hassan Saeed Hassan Albattra, Mazen Mohammed Bahgat, Rahatara Ferdousi et al.