Liling Tan

838 citations
46 papers · 521 · h-index 12

Impact in

    • Natural Language Processing Techniques
    • Topic Modeling
    • Authorship Attribution and Profiling
    • Text Readability and Simplification
    • Text and Document Classification Technologies
    • Fatty Acid Research and Health

Papers in

    • Natural Language Processing Techniques 30
    • Topic Modeling 26
    • Text Readability and Simplification 8
    • Advanced Text Analysis Techniques 4
    • Biomedical Text Mining and Ontologies 5

Liling Tan

44 papers receiving 467 citations

Peers

Liling Tan
Comparison fields: 5 of 87
  • Artificial Intelligence 340
  • Nutrition and Dietetics 88
  • Linguistics and Language 19
  • Biochemistry 20
  • Language and Linguistics 21
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Citations per field
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Citations per year

Countries citing papers authored by Liling Tan

Since Specialization
Citations

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

Fields of papers citing papers by Liling Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Liling Tan. 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 Liling Tan. The network helps show where Liling Tan may publish in the future.

Co-authors

The 25 scholars most cited alongside Liling Tan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Liling Tan Line = papers co-authored together Liling Tan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 46 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1995103
2 201472
3
Overview of the DSL Shared Task 2015
201570
4 202038
5 202419
6
Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)
201617
7 201516
8 201616
9 201413
10 201612
11 202011
12
An Awkward Disparity between BLEU / RIBES Scores and Human Judgements in Machine Translation
201511
13 202310
14 202310
15 201510
16 20248
17 20158
18 20227
19
Building and Annotating the Linguistically Diverse NTU-MC (NTU - Multilingual Corpus).
20116
20 20166

About Liling Tan

Liling Tan is a scholar working on Artificial Intelligence, Molecular Biology, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Endocrinology, Diabetes and Metabolism, having authored 46 papers that have together received 521 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (30 papers), Topic Modeling (26 papers), Text Readability and Simplification (8 papers), Biomedical Text Mining and Ontologies (5 papers), Multimodal Machine Learning Applications (4 papers), Advanced Text Analysis Techniques (4 papers), Thyroid Cancer Diagnosis and Treatment (4 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). The work is most often cited by research in Artificial Intelligence (340 citations), Nutrition and Dietetics (88 citations), Linguistics and Language (19 citations), Biochemistry (20 citations) and Language and Linguistics (21 citations). Liling Tan has collaborated with scholars based in Germany, China and Singapore. Frequent co-authors include Marcos Zampieri, A S Truswell, Josef van Genabith, Jörg Tiedemann, Nikola Ljubešić, Preslav Nakov, Francis Bond, Raymond Hendy Susanto, Shamil Chollampatt and Santanu Pal. Their work appears in journals such as Journal of Cold Regions Engineering, Cold Regions Science and Technology, Functional & Integrative Genomics, Frontiers in Earth Science and Geoderma.

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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