Donghong Ji
- Artificial Intelligence top 0.5%
- Information Systems top 1%
- Sociology and Political Science top 10%
- Signal Processing top 5%
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Yafeng RenMeishan ZhangHao FeiYue ZhangZheng-Yu NiuHao TangQiji ZhouFei Li
- Topics
- Topic Modeling (73 papers)Natural Language Processing Techniques (52 papers)Sentiment Analysis and Opinion Mining (43 papers)
- Partner nations
- ChinaSingaporeUnited States
In The Last Decade
Donghong Ji
101 papers receiving 2.2k citations
Peers
Comparison fields: 5 of 96
- Artificial Intelligence 2.0k
- Information Systems 499
- Sociology and Political Science 216
- Signal Processing 115
- Computer Vision and Pattern Recognition 111
Countries citing papers authored by Donghong Ji
This map shows the geographic impact of Donghong 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 Donghong Ji with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Donghong Ji more than expected).
Fields of papers citing papers by Donghong Ji
This network shows the impact of papers produced by Donghong 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 Donghong Ji. The network helps show where Donghong Ji may publish in the future.
Co-authorship network of co-authors of Donghong Ji
This figure shows the co-authorship network connecting the top 25 collaborators of Donghong Ji. A scholar is included among the top collaborators of Donghong Ji 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 Donghong Ji. Donghong Ji is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 6 | |
| 6 | 1 | |
| 7 | 8 | |
| 8 | 3 | |
| 9 | 13 | |
| 10 | 55 | |
| 11 | 232 | |
| 12 | Joint models for extracting adverse drug events from biomedical text | 39 |
| 13 | Distance Metric Learning for Aspect Phrase Grouping | 0 |
| 14 | Word Sense Induction Using Lexical Chain based Hypergraph Model | 4 |
| 15 | Multi-Strategy Question Answering System for NTCIR-7 C-C Task. | 3 |
| 16 | Tree Kernel-Based Relation Extraction with Context-Sensitive Structured Parse Tree Information | 127 |
| 17 | Chinese Word Segmentation and Named Entity Recognition Based on a Context-Dependent Mutual Information Independence Model | 6 |
| 18 | Optimizing feature set for Chinese Word Sense Disambiguation | 7 |
| 19 | Chinese Language IR based on Term Extraction | 1 |
| 20 | Learning New Compositions from Given Ones | 1 |
About Donghong Ji
Donghong Ji is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition, having authored 112 papers that have together received 2.3k indexed citations. Recurring topics across this work include Topic Modeling (73 papers), Natural Language Processing Techniques (52 papers) and Sentiment Analysis and Opinion Mining (43 papers). The work is most often cited by research in Artificial Intelligence (2.0k citations), Information Systems (499 citations) and Signal Processing (115 citations). Donghong Ji has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Yafeng Ren, Meishan Zhang, Hao Fei, Yue Zhang, Zheng-Yu Niu, Hao Tang, Qiji Zhou, Fei Li, Chew Lim Tan and Chenliang Li. Their work appears in journals such as PLoS ONE, Scientific Reports and IEEE Access.
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.