SZTE-NLP at SemEval-2017 Task 10: A High Precision Sequence Model for Keyphrase Extraction Utilizing Sparse Coding for Feature Generation
Gábor Berend · 2017
In this paper we introduce our system participating at the 2017 SemEval shared task on keyphrase extraction from scientific documents.We aimed at the creation of a keyphrase extraction approach which relies on as little external resources as possible.Without applying any hand-crafted external resources, and only utilizing a transformed version of word embeddings trained at Wikipedia, our proposed system manages to perform among the best participating systems in terms of precision.