Heng Ji
- Artificial Intelligence top 0.2%
- Topic Modeling 117
- Natural Language Processing Techniques 87
- Advanced Text Analysis Techniques 26
- Semantic Web and Ontologies 17
- Text and Document Classification Technologies 16
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- Data Quality and Management 14
- Information Systems top 1%
- Web Data Mining and Analysis 15
- Communication top 5%
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- Multimodal Machine Learning Applications 15
- Cited by
- Artificial IntelligenceManagement Science and Operations ResearchComputer Science Applications
- Partner nations
- United StatesChinaFrance
In The Last Decade
Heng Ji
152 papers receiving 4.3k citations
Hit Papers
Peers
Comparison fields: 5 of 114
- Artificial Intelligence 4.0k
- Management Science and Operations Research 572
- Computer Science Applications 199
- Information Systems 780
- Communication 143
Countries citing papers authored by Heng Ji
This map shows the geographic impact of Heng Ji'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 Heng Ji with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Heng Ji more than expected).
Fields of papers citing papers by Heng Ji
This network shows the impact of papers produced by Heng Ji. 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 Heng Ji. The network helps show where Heng Ji may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Heng Ji, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2024 | 6 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 6 | |
| 6 | 2024 | 9 | |
| 7 | 2023 | 6 | |
| 8 | 2023 | 18 | |
| 9 | 2023 | 5 | |
| 10 | 2022 | 3 | |
| 11 | 2022 | 5 | |
| 12 | 2021 | 134 | |
| 13 | 2021 | 13 | |
| 14 | A Joint Neural Model for Information Extraction with Global Featuresbreakdown → | 2020 | 225 |
| 15 | 2018 | 117 | |
| 16 | Learning Phrase Embeddings from Paraphrases with GRUs | 2017 | 4 |
| 17 | Embracing Non-Traditional Linguistic Resources for Low-resource Language Name Tagging | 2017 | 6 |
| 18 | Bitext Name Tagging for Cross-lingual Entity Annotation Projection | 2016 | 7 |
| 19 | Comparison of the Impact of Word Segmentation on Name Tagging for Chinese and Japanese | 2014 | 40 |
| 20 | Studies for hibernant habitat and hibernant habit of Paederus fuscipes Curtis | 2000 | 3 |
About Heng Ji
Heng Ji is a scholar working on Artificial Intelligence, Information Systems and Management Science and Operations Research, having authored 162 papers that have together received 4.6k indexed citations. Recurring topics across this work include Topic Modeling (117 papers), Natural Language Processing Techniques (87 papers), Advanced Text Analysis Techniques (26 papers), Semantic Web and Ontologies (17 papers), Text and Document Classification Technologies (16 papers), Multimodal Machine Learning Applications (15 papers), Web Data Mining and Analysis (15 papers) and Data Quality and Management (14 papers). The work is most often cited by research in Artificial Intelligence (4.0k citations), Management Science and Operations Research (572 citations) and Computer Science Applications (199 citations). Heng Ji has collaborated with scholars based in United States, China and France. Frequent co-authors include Qi Li, Ralph Grishman, Jiawei Han, Liang Huang, Lifu Huang, Ying Lin, Clare R. Voss, Lingfei Wu, Fei Huang and Sha Li. Their work appears in journals such as Biomaterials, Knowledge-Based Systems and Nature Human Behaviour.
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.