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.
This map shows the geographic impact of Chin-Yew Lin'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 Chin-Yew Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chin-Yew Lin more than expected).
This network shows the impact of papers produced by Chin-Yew Lin. 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 Chin-Yew Lin. The network helps show where Chin-Yew Lin may publish in the future.
Co-authorship network of co-authors of Chin-Yew Lin
This figure shows the co-authorship network connecting the top 25 collaborators of Chin-Yew Lin.
A scholar is included among the top collaborators of Chin-Yew Lin 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 Chin-Yew Lin. Chin-Yew Lin is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Huang, Danqing, Jing Liu, Chin-Yew Lin, & Jian Yin. (2018). Neural Math Word Problem Solver with Reinforcement Learning. International Conference on Computational Linguistics. 213–223.41 indexed citations
Xin, Xin, et al.. (2015). Cross-domain collaborative filtering with review text. International Conference on Artificial Intelligence. 1827–1833.22 indexed citations
Zhang, Fan, et al.. (2011). Nonlinear Evidence Fusion and Propagation for Hyponymy Relation Mining. Meeting of the Association for Computational Linguistics. 1159–1168.13 indexed citations
11.
Zhang, Wei, Chew Lim Tan, Jian Su, et al.. (2011). I2R-NUS-MSRA at TAC 2011: Entity Linking.. Theory and applications of categories.9 indexed citations
12.
Duan, Huizhong, Yunbo Cao, Chin-Yew Lin, & Yong Yu. (2008). Searching Questions by Identifying Question Topic and Question Focus. Meeting of the Association for Computational Linguistics. 156–164.98 indexed citations
13.
Yu, Liang-Chih, et al.. (2007). Topic Analysis for Psychiatric Document Retrieval. Meeting of the Association for Computational Linguistics. 1024–1031.2 indexed citations
14.
Hovy, Eduard, Chin-Yew Lin, Liang Zhou, & Junichi Fukumoto. (2006). Automated Summarization Evaluation with Basic Elements.. Language Resources and Evaluation. 899–902.99 indexed citations
15.
Lin, Chin-Yew. (2004). Looking for a Few Good Metrics: Automatic Summarization Evaluation — How Many Samples Are Enough?. NTCIR.58 indexed citations
Hovy, Eduard, Ulf Hermjakob, Chin-Yew Lin, & Deepak Ravichandran. (2002). Using Knowledge to Facilitate Pinpointing of Factoid Answers. International Conference on Computational Linguistics.8 indexed citations
18.
Hovy, Eduard, Ulf Hermjakob, & Chin-Yew Lin. (2001). The use of external knowledge in factoid QA. Text REtrieval Conference. 644–652.76 indexed citations
19.
Hovy, Eduard, Laurie Gerber, Ulf Hermjakob, Michael Junk, & Chin-Yew Lin. (2000). Question Answering in Webclopedia.. Text REtrieval Conference.164 indexed citations
20.
Lin, Chin-Yew. (1998). Assembly of Topic Extraction Modules in SUMMARIST. National Conference on Artificial Intelligence.6 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.