NLP-CIC @ PRELEARN: Mastering prerequisites relations, from handcrafted features to embeddings
This work addresses a specific NLP task for educational or knowledge organization domains, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of classifying prerequisite relations between concepts, achieving first place in both in-domain and cross-domain scenarios with average F1 scores of 0.887 and 0.690, respectively.
We present our systems and findings for the prerequisite relation learning task (PRELEARN) at EVALITA 2020. The task aims to classify whether a pair of concepts hold a prerequisite relation or not. We model the problem using handcrafted features and embedding representations for in-domain and cross-domain scenarios. Our submissions ranked first place in both scenarios with average F1 score of 0.887 and 0.690 respectively across domains on the test sets. We made our code is freely available.