Negation Detection Using NooJ

The availability of extensive annotated data for natural language processing tasks is an unsolved problem. Transfer learning techniques usually mitigate these issues by relying on existing models in another language. If no such models exist, the whole transfer learning setup becomes an implausible option. This paper presents a simple approach to use grammar rule as a noisy labelling function to train a classic generative-discriminative classification setup. The approach relies on a simple NooJ grammar along with a series of other data labelling functions. We evaluate the approach on the Conan-Doyle dataset for the task of explicit negation detection with a low-resource setting and report an improvement of 2% over the baseline.