Identifying Emergent Research Trends by Key Authors and Phrases

Shenhao Jiang, Animesh Prasad, Min-Yen Kan, Kazunari Sugiyama


Abstract
Identifying emergent research trends is a key issue for both primary researchers as well as secondary research managers. Such processes can uncover the historical development of an area, and yield insight on developing topics. We propose an embedded trend detection framework for this task which incorporates our bijunctive hypothesis that important phrases are written by important authors within a field and vice versa. By ranking both author and phrase information in a multigraph, our method jointly determines key phrases and authoritative authors. We represent this intermediate output as phrasal embeddings, and feed this to a recurrent neural network (RNN) to compute trend scores that identify research trends. Over two large datasets of scientific articles, we demonstrate that our approach successfully detects past trends from the field, outperforming baselines based solely on text centrality or citation.
Anthology ID:
C18-1022
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
259–269
Language:
URL:
https://www.aclweb.org/anthology/C18-1022
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/C18-1022.pdf