Research Area: Machine Learning
Keyphrase extraction is a fundamental task in natural language processing that facilitates mapping of documents to a set of representative phrases. In this paper, we present an unsupervised technique (Key2Vec) that leverages phrase embeddings for ranking keyphrases extracted from scientific articles. Specifically, we propose an effective way of processing text documents for training multi-word phrase embeddings that are used for thematic representation of scientific articles and ranking of keyphrases extracted from them using theme-weighted PageRank. Evaluations are performed on benchmark datasets producing state-of-the-art results.
Keywords:
Ranked Keyphrase Extraction
Scientific Articles
Phrase Embeddings
unsupervised technique
natural language processing
Machine Learning
Deep Learning
Author(s) Name: Debanjan Mahata, John Kuriakose, Rajiv Ratn Shah, Roger Zimmermann
Journal name:
Conferrence name: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Publisher name: Association for Computational Linguistics
DOI: 10.18653/v1/N18-2100
Volume Information:
Paper Link: https://aclanthology.org/N18-2100/
