Minimally supervised dependency-based methods for natural language processing

Marek Rei
This work investigates minimally-supervised methods for solving NLP tasks, without requiring explicit annotation or training data. Our motivation is to create systems that require substantially reduced effort from domain and/or NLP experts, compared to annotating a corresponding dataset, and also offer easier domain adaptation and better generalisation properties. We apply these principles to four separate language processing tasks and analyse their performance compared to supervised alternatives. First, we investigate the task of detecting the scope...
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