Guohong Fu

1.8k total citations
62 papers, 969 citations indexed

About

Guohong Fu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Guohong Fu has authored 62 papers receiving a total of 969 indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 6 papers in Molecular Biology. Recurrent topics in Guohong Fu's work include Topic Modeling (47 papers), Natural Language Processing Techniques (42 papers) and Sentiment Analysis and Opinion Mining (12 papers). Guohong Fu is often cited by papers focused on Topic Modeling (47 papers), Natural Language Processing Techniques (42 papers) and Sentiment Analysis and Opinion Mining (12 papers). Guohong Fu collaborates with scholars based in China, Hong Kong and Singapore. Guohong Fu's co-authors include Meishan Zhang, Yue Zhang, Fei Li, Kang Kwong Luke, Yue Zhang, Nan Yu, Xin Wang, Xin Wang, Min Zhang and Yang Sun and has published in prestigious journals such as SHILAP Revista de lepidopterología, BMC Bioinformatics and Artificial Intelligence.

In The Last Decade

Guohong Fu

57 papers receiving 909 citations

Peers

Guohong Fu
Comparison fields: 5 of 77
  • Artificial Intelligence 905
  • Molecular Biology 140
  • Computer Vision and Pattern Recognition 112
  • Information Systems 96
  • Management Science and Operations Research 61
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Citations per field, relative to Guohong Fu
Guohong Fu · 1×
Citations per year, relative to Guohong Fu
Guohong Fu · 1×

Countries citing papers authored by Guohong Fu

Since Specialization
Citations

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

Fields of papers citing papers by Guohong Fu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Guohong Fu

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

All Works

20 of 20 papers shown
# Work Indexed citations
1 0
2 0
3 0
4 3
5 1
6 6
7
The Value of Serum miR-139-3p Expression Level in Predicting Postoperative Survival of Colon Cancer Patients
2
8 16
9 25
10
Transition-based Neural RST Parsing with Implicit Syntax Features
37
11 161
12
Tweet Sarcasm Detection Using Deep Neural Network
91
13 4
14
A CRF Sequence Labeling Approach to Chinese Punctuation Prediction
5
15
Chinese Sentence-Level Sentiment Classification Based on Fuzzy Sets
45
16
A Morpheme-based Part-of-speech Tagger for Chinese
3
17
Chinese Unknown Word Identification as Known Word Tagging.
1
18 2
19 4
20
An integrated approach for Chinese word segmentation
7

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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