SEAL: Scientific Keyphrase Extraction and Classification
Ayush Garg, Sammed Shantinath Kagi, Mayank Singh

TL;DR
SEAL is a neural and machine learning-based tool designed for automatic extraction and classification of scientific keyphrases, significantly improving over existing methods and supporting scholarly tasks.
Contribution
Introduces SEAL, combining a neural extraction module with a Random Forest classifier for effective scientific keyphrase extraction and classification.
Findings
SEAL outperforms multiple state-of-the-art baselines
The system demonstrates robustness across various datasets
Significant improvement in keyphrase extraction accuracy
Abstract
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/.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
