NLP-CIC @ PRELEARN: Mastering prerequisites relations, from handcrafted features to embeddings

NLP-CIC @ PRELEARN: Mastering prerequisites relations, from handcrafted features to embeddings

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-...

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Hauptverfasser: Angel, Jason, Aroyehun, Segun Taofeek, Gelbukh, Alexander
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Computer Science - Artificial Intelligence
Computer Science - Computation and Language
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creator Angel, Jason
Aroyehun, Segun Taofeek
Gelbukh, Alexander
description 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.
doi_str_mv 10.48550/arxiv.2011.03760
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title NLP-CIC @ PRELEARN: Mastering prerequisites relations, from handcrafted features to embeddings
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