Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey
2023577 citationsBonan Min, Hayley Ross et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Bonan Min's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Bonan Min with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bonan Min more than expected).
This network shows the impact of papers produced by Bonan Min. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Bonan Min. The network helps show where Bonan Min may publish in the future.
Co-authorship network of co-authors of Bonan Min
This figure shows the co-authorship network connecting the top 25 collaborators of Bonan Min.
A scholar is included among the top collaborators of Bonan Min based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Bonan Min. Bonan Min is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Min, Bonan, Yee Seng Chan, & Lingjun Zhao. (2020). Towards Few-Shot Event Mention Retrieval: An Evaluation Framework and A Siamese Network Approach. Language Resources and Evaluation. 1747–1752.2 indexed citations
Min, Bonan, et al.. (2018). When ACE met KBP: End-to-End Evaluation of Knowledge Base Population with Component-level Annotation.. Language Resources and Evaluation.1 indexed citations
13.
Min, Bonan, Zhuolin Jiang, Marjorie Freedman, & Ralph Weischedel. (2017). Learning Transferable Representation for Bilingual Relation Extraction via Convolutional Neural Networks. International Joint Conference on Natural Language Processing. 1. 674–684.4 indexed citations
14.
Nguyen, Thien Huu, et al.. (2017). Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network. International Joint Conference on Natural Language Processing. 2. 425–429.29 indexed citations
Min, Bonan, Ralph Grishman, Li Wan, Chang Wang, & David Gondek. (2013). Distant Supervision for Relation Extraction with an Incomplete Knowledge Base. North American Chapter of the Association for Computational Linguistics. 777–782.135 indexed citations
17.
Min, Bonan & Ralph Grishman. (2012). Compensating for Annotation Errors in Training a Relation Extractor. Conference of the European Chapter of the Association for Computational Linguistics. 194–203.2 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.