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Title: Identifying Emergent Research Trends by Key Authors and Phrases
Authors: Shenhao Jiang 
Animesh Prasad 
Min-Yen Kan 
Kazunari Sugiyama 
Issue Date: 2018
Publisher: Association for Computational Linguistics
Citation: Shenhao Jiang, Animesh Prasad, Min-Yen Kan, Kazunari Sugiyama (2018). Identifying Emergent Research Trends by Key Authors and Phrases. Proceedings of the 27th International Conference on Computational Linguistics (COLING 2018 : 259-269. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
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.
Source Title: Proceedings of the 27th International Conference on Computational Linguistics (COLING 2018
Rights: Attribution 4.0 International
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