TY - GEN
T1 - MayoNLP at SemEval 2017 Task 10
T2 - 11th International Workshop on Semantic Evaluations, SemEval 2017, co-located with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017
AU - Liu, Sijia
AU - Shen, Feichen
AU - Chaudhary, Vipin
AU - Liu, Hongfang
N1 - Funding Information:
We would like to thank Yanshan Wang, Raviku-mar Komandur Elayavilli, and Majid Rastegar-Mojarad for their valuable suggestions. This work is supported by NIH grants R01GM102282-01A1 and R01EB19403-01 and NSF IPA grant.
Publisher Copyright:
© 2017 Association for Computational Linguistics
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85086825994&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85086825994&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85086825994
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 956
EP - 960
BT - ACL 2017 - 11th International Workshop on Semantic Evaluations, SemEval 2017, Proceedings of the Workshop
PB - Association for Computational Linguistics (ACL)
Y2 - 3 August 2017 through 4 August 2017
ER -