Siew Mei Wu

8 papers receiving 533 citations

Hit Papers

The CoNLL-2014 Shared Task on Grammatical Error Correction20142026201820222014100200300

Peers

Siew Mei Wu
Comparison fields: 5 of 38
  • Artificial Intelligence 580
  • Information Systems 48
  • Computer Vision and Pattern Recognition 42
  • Developmental and Educational Psychology 31
  • Language and Linguistics 19
Replace Øistein E. Andersen with:
Øistein E. Andersen United Kingdom
Courtney Napoles United States
Christopher Bryant United Kingdom
Svetoslav Marinov Sweden
Satoshi Sato Japan
Markus Dickinson United States
Anna Feldman United States
Raymond Hendy Susanto Singapore
Atanas Chanev Sweden
Christof Müller Germany
Siew Mei Wu relative to Øistein E. Andersen United Kingdom Øistein E. Andersen's profile →
Citations per field
00.5×3.2×
Øistein E. Andersen · 1×
Citations per year

Countries citing papers authored by Siew Mei Wu

Since Specialization
Citations

This map shows the geographic impact of Siew Mei Wu'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 Siew Mei Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Siew Mei Wu more than expected).

Fields of papers citing papers by Siew Mei Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Siew Mei Wu. 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 Siew Mei Wu. The network helps show where Siew Mei Wu may publish in the future.

Co-authorship network of co-authors of Siew Mei Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Siew Mei Wu. A scholar is included among the top collaborators of Siew Mei Wu 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 Siew Mei Wu. Siew Mei Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

9 of 9 papers shown
#WorkIndexed citations
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2 1
3 3
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Teaching Academic Literacy Using Popular Science Texts: A Case Study.
0
5 10
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The CoNLL-2014 Shared Task on Grammatical Error Correctionbreakdown →
353
7
Building a Large Annotated Corpus of Learner English: The NUS Corpus of Learner English
232
8
Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task
15
9
INVESTIGATING THE EFFECTIVENESS OF ARGUMENTS IN UNDERGRADUATE ESSAYS FROM AN EVALUATION PERSPECTIVE
8

About Siew Mei Wu

Siew Mei Wu is a scholar working on Literature and Literary Theory, Developmental and Educational Psychology and Visual Arts and Performing Arts, having authored 9 papers that have together received 624 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Natural Language Processing Techniques (3 papers) and Discourse Analysis in Language Studies (3 papers). The work is most often cited by research in Artificial Intelligence (580 citations), Developmental and Educational Psychology (31 citations) and Information Systems (48 citations). Siew Mei Wu has collaborated with scholars based in Singapore, United Kingdom and Australia. Frequent co-authors include Hwee Tou Ng, Daniel Dahlmeier, Raymond Hendy Susanto, Christopher Bryant, Christian Hadiwinoto, Ted Briscoe, Sze Han Lee, Enikó Csomay, Eric Chun Yong Chan and Henk Huijser. Their work appears in journals such as Higher Education Research & Development, Teaching & Learning Inquiry The ISSOTL Journal and National University of Singapore.

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.

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