SEAL: Scientific Keyphrase Extraction and Classification
This work addresses the challenge of keyphrase extraction for scholarly tasks like search and recommendation, but it appears incremental as it combines existing methods (BiLSTM-CRF and Random Forest) without a major paradigm shift.
The authors tackled the problem of automatic scientific keyphrase extraction and classification by introducing SEAL, a tool that uses a two-stage neural architecture with BiLSTM-CRF for extraction and a Random Forest classifier for classification, achieving significant improvements over state-of-the-art baselines.
Automatic scientific keyphrase extraction is a challenging problem facilitating several downstream scholarly tasks like search, recommendation, and ranking. In this paper, we introduce SEAL, a scholarly tool for automatic keyphrase extraction and classification. The keyphrase extraction module comprises two-stage neural architecture composed of Bidirectional Long Short-Term Memory cells augmented with Conditional Random Fields. The classification module comprises of a Random Forest classifier. We extensively experiment to showcase the robustness of the system. We evaluate multiple state-of-the-art baselines and show a significant improvement. The current system is hosted at http://lingo.iitgn.ac.in:5000/.