Abstract
In this paper, we present MayoNLP’s results from the participation in the ScienceIE share task at SemEval 2017. We focused on the keyphrase classification task (Subtask B). We explored semantic similarities and patterns of keyphrases in scientific publications using pre-trained word embedding models. Word Embedding Distance Pattern, which uses the head noun word embedding to generate distance patterns based on labeled keyphrases, is proposed as an incremental feature set to enhance the conventional Named Entity Recognition feature sets. Support vector machine is used as the supervised classifier for keyphrase classification. Our system achieved an overall F1 score of 0.67 for keyphrase classification and 0.64 for keyphrase classification and relation detection.- Anthology ID:
- S17-2166
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 956–960
- Language:
- URL:
- https://aclanthology.org/S17-2166
- DOI:
- 10.18653/v1/S17-2166
- Cite (ACL):
- Sijia Liu, Feichen Shen, Vipin Chaudhary, and Hongfang Liu. 2017. MayoNLP at SemEval 2017 Task 10: Word Embedding Distance Pattern for Keyphrase Classification in Scientific Publications. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 956–960, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- MayoNLP at SemEval 2017 Task 10: Word Embedding Distance Pattern for Keyphrase Classification in Scientific Publications (Liu et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/S17-2166.pdf