Nancy Chang
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Experimental and Cognitive Psychology
- Language and Linguistics top 5%
- Information Systems
- Co-authors
- Richard SproatWilliam A. GaleChilin ShihTiago V. MaiaJerome A. FeldmanBenjamin K. BergenSrini NarayananSu Wang
- Topics
- Natural Language Processing Techniques (12 papers)Topic Modeling (7 papers)Syntax, Semantics, Linguistic Variation (6 papers)
- Journals
- Computational LinguisticsAI MagazineeScholarship (California Digital Library)
- Partner nations
- United StatesJapanEcuador
In The Last Decade
Nancy Chang
18 papers receiving 377 citations
Peers
Comparison fields: 5 of 42
- Artificial Intelligence 356
- Computer Vision and Pattern Recognition 75
- Experimental and Cognitive Psychology 62
- Language and Linguistics 58
- Information Systems 44
Countries citing papers authored by Nancy Chang
This map shows the geographic impact of Nancy Chang'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 Nancy Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nancy Chang more than expected).
Fields of papers citing papers by Nancy Chang
This network shows the impact of papers produced by Nancy Chang. 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 Nancy Chang. The network helps show where Nancy Chang may publish in the future.
Co-authorship network of co-authors of Nancy Chang
This figure shows the co-authorship network connecting the top 25 collaborators of Nancy Chang. A scholar is included among the top collaborators of Nancy Chang 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 Nancy Chang. Nancy Chang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 36 | |
| 2 | 1 | |
| 3 | 5 | |
| 4 | 6 | |
| 5 | 12 | |
| 6 | 30 | |
| 7 | Constructing grammar: a computational model of the emergence of early constructions | 26 |
| 8 | 3 | |
| 9 | 8 | |
| 10 | 34 | |
| 11 | Context-Driven Construction Learning | 8 |
| 12 | Putting Meaning into Grammar Learning | 7 |
| 13 | 21 | |
| 14 | Learning Grammatical Constructions | 15 |
| 15 | Grounding the Acquisition of Grammar in Sensorimotor Representations | 2 |
| 16 | Grounded Learning of Grammatical Constructions | 15 |
| 17 | Understanding Idioms | 2 |
| 18 | 208 |
About Nancy Chang
Nancy Chang is a scholar working on Language and Linguistics, Artificial Intelligence and Computer Science Applications, having authored 18 papers that have together received 439 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (12 papers), Topic Modeling (7 papers) and Syntax, Semantics, Linguistic Variation (6 papers). The work is most often cited by research in Artificial Intelligence (356 citations), Language and Linguistics (58 citations) and Experimental and Cognitive Psychology (62 citations). Nancy Chang has collaborated with scholars based in United States, Japan and Ecuador. Frequent co-authors include Richard Sproat, William A. Gale, Chilin Shih, Tiago V. Maia, Jerome A. Feldman, Benjamin K. Bergen, Srini Narayanan, Su Wang, Jason Baldridge and Rahul Gupta. Their work appears in journals such as Computational Linguistics, AI Magazine and eScholarship (California Digital Library).
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.