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.
Supervised and Traditional Term Weighting Methods for Automatic Text Categorization
2009379 citationsMan Lan, Chew Lim Tan 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 Man Lan'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 Man Lan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Man Lan more than expected).
This network shows the impact of papers produced by Man Lan. 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 Man Lan. The network helps show where Man Lan may publish in the future.
Co-authorship network of co-authors of Man Lan
This figure shows the co-authorship network connecting the top 25 collaborators of Man Lan.
A scholar is included among the top collaborators of Man Lan 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 Man Lan. Man Lan is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lan, Man, et al.. (2015). Leverage Web-based Answer Retrieval and Hierarchical Answer Selection to Improve the Performance of Live Question Answering.. Text REtrieval Conference.1 indexed citations
9.
Zhang, Zhihua & Man Lan. (2014). Estimating Semantic Similarity between Expanded Query and Tweet Content for Microblog Retrieval.. Text REtrieval Conference.3 indexed citations
10.
Jin, S. & Man Lan. (2014). Simple May Be Best - A Simple and Effective Method for Federated Web Search via Search Engine Impact Factor Estimation.. Text REtrieval Conference.6 indexed citations
11.
Lan, Man, et al.. (2013). ECNUCS: Measuring Short Text Semantic Equivalence Using Multiple Similarity Measurements. Joint Conference on Lexical and Computational Semantics. 1. 124–131.6 indexed citations
12.
Lan, Man, Yu Xu, & Zheng-Yu Niu. (2013). Leveraging Synthetic Discourse Data via Multi-task Learning for Implicit Discourse Relation Recognition. Meeting of the Association for Computational Linguistics. 476–485.32 indexed citations
13.
Mohtarami, Mitra, Man Lan, & Chew Lim Tan. (2013). Probabilistic Sense Sentiment Similarity through Hidden Emotions. Meeting of the Association for Computational Linguistics. 983–992.5 indexed citations
14.
Lan, Man, et al.. (2013). ECNUCS: A Surface Information Based System Description of Sentiment Analysis in Twitter in the SemEval-2013 (Task 2). Joint Conference on Lexical and Computational Semantics. 408–413.6 indexed citations
15.
Zhao, Jiang, Man Lan, & Zheng-Yu Niu. (2013). ECNUCS: Recognizing Cross-lingual Textual Entailment Using Multiple Text Similarity and Text Difference Measures. Joint Conference on Lexical and Computational Semantics. 2. 118–123.4 indexed citations
16.
Zhou, Zhimin, Yu Xu, Zheng-Yu Niu, et al.. (2010). Predicting Discourse Connectives for Implicit Discourse Relation Recognition. National University of Singapore. 1507–1514.76 indexed citations
17.
Lan, Man, et al.. (2010). ECNU: Effective Semantic Relations Classification without Complicated Features or Multiple External Corpora. Meeting of the Association for Computational Linguistics. 226–229.4 indexed citations
Lan, Man, Chew-Lim Tan, & Hwee-Boon Low. (2006). Proposing a new term weighting scheme for text categorization. National University of Singapore. 763–768.64 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.