Abstract
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.- Anthology ID:
- S17-2173
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 990–994
- Language:
- URL:
- https://aclanthology.org/S17-2173
- DOI:
- 10.18653/v1/S17-2173
- Cite (ACL):
- Gábor Berend. 2017. SZTE-NLP at SemEval-2017 Task 10: A High Precision Sequence Model for Keyphrase Extraction Utilizing Sparse Coding for Feature Generation. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 990–994, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- SZTE-NLP at SemEval-2017 Task 10: A High Precision Sequence Model for Keyphrase Extraction Utilizing Sparse Coding for Feature Generation (Berend, SemEval 2017)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/S17-2173.pdf
- Data
- SemEval-2017 Task-10