Shi-Zhe Huang
- Artificial Intelligence top 10%
- Language and Linguistics top 5%
- Linguistics and Language top 10%
- Experimental and Cognitive Psychology
- Mechanical Engineering
- Co-authors
- Fei XiaNianwen XueAnthony KrochFu-Dong ChiouMitchell P. MarcusMary Ellen OkurowskiMartha PalmerRixin Li
- Topics
- Natural Language Processing Techniques (7 papers)Syntax, Semantics, Linguistic Variation (5 papers)Linguistic Variation and Morphology (2 papers)
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Shi-Zhe Huang
12 papers receiving 190 citations
Peers
Comparison fields: 5 of 33
- Artificial Intelligence 182
- Language and Linguistics 89
- Linguistics and Language 28
- Experimental and Cognitive Psychology 23
- Mechanical Engineering 15
Countries citing papers authored by Shi-Zhe Huang
This map shows the geographic impact of Shi-Zhe Huang'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 Shi-Zhe Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shi-Zhe Huang more than expected).
Fields of papers citing papers by Shi-Zhe Huang
This network shows the impact of papers produced by Shi-Zhe Huang. 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 Shi-Zhe Huang. The network helps show where Shi-Zhe Huang may publish in the future.
Co-authorship network of co-authors of Shi-Zhe Huang
This figure shows the co-authorship network connecting the top 25 collaborators of Shi-Zhe Huang. A scholar is included among the top collaborators of Shi-Zhe Huang 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 Shi-Zhe Huang. Shi-Zhe Huang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 19 | |
| 3 | 17 | |
| 4 | Nominal Modification in Chinese and Thai | 2 |
| 5 | Looking into Clauses | 2 |
| 6 | Henda Guwu: More on the Type Matching Constraint on Modification | 2 |
| 7 | 33 | |
| 8 | Universal Quantification With Skolemization As Evidenced In Chinese and English | 5 |
| 9 | Developing Guidelines and Ensuring Consistency for Chinese Text Annotation | 89 |
| 10 | The Bracketing Guidelines for the Penn Chinese Treebank (3.0) | 43 |
| 11 | The Bracketing Guidelines for the Chinese Treebank | 4 |
| 12 | Quantification and Predication in Mandarin Chinese: A Case Study of Dou | 25 |
| 13 | Dou as an Existential Quantifier | 3 |
About Shi-Zhe Huang
Shi-Zhe Huang is a scholar working on Language and Linguistics, Linguistics and Language and Artificial Intelligence, having authored 13 papers that have together received 245 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (7 papers), Syntax, Semantics, Linguistic Variation (5 papers) and Linguistic Variation and Morphology (2 papers). The work is most often cited by research in Language and Linguistics (89 citations), Linguistics and Language (28 citations) and Artificial Intelligence (182 citations). Shi-Zhe Huang has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Fei Xia, Nianwen Xue, Anthony Kroch, Fu-Dong Chiou, Mitchell P. Marcus, Mary Ellen Okurowski, Martha Palmer, Rixin Li, Fayong Zhang and Jinping Liu. Their work appears in journals such as Applied Thermal Engineering, Applied Geography and Language Resources and Evaluation.
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.