Daniel Jurafsky
About
In The Last Decade
Daniel Jurafsky
93 papers receiving 9.5k citations
Hit Papers
Peers
Comparison fields: 5 of 165
- Artificial Intelligence 8.7k
- Information Systems 1.2k
- Experimental and Cognitive Psychology 955
- Language and Linguistics 908
- Computer Science Applications 838
Countries citing papers authored by Daniel Jurafsky
This map shows the geographic impact of Daniel Jurafsky'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 Daniel Jurafsky with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Jurafsky more than expected).
Fields of papers citing papers by Daniel Jurafsky
This network shows the impact of papers produced by Daniel Jurafsky. 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 Daniel Jurafsky. The network helps show where Daniel Jurafsky may publish in the future.
Co-authorship network of co-authors of Daniel Jurafsky
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Jurafsky. A scholar is included among the top collaborators of Daniel Jurafsky 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 Daniel Jurafsky. Daniel Jurafsky 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 | Same Referent, Different Words: Unsupervised Mining of Opaque Coreferent Mentions | 15 |
| 3 | Emergence of Gricean Maxims from Multi-Agent Decision Theory | 23 |
| 4 | Capitalization Cues Improve Dependency Grammar Induction | 4 |
| 5 | Parsing Time: Learning to Interpret Time Expressions | 30 |
| 6 | Bootstrapping Dependency Grammar Inducers from Incomplete Sentence Fragments via Austere Models | 3 |
| 7 | Lateen EM: Unsupervised Training with Multiple Objectives, Applied to Dependency Grammar Induction | 19 |
| 8 | A study of academic collaboration in computational linguistics with latent mixtures of authors | 7 |
| 9 | Profiting from Mark-Up: Hyper-Text Annotations for Guided Parsing | 24 |
| 10 | Parsing to Stanford Dependencies: Trade-offs between Speed and Accuracy. | 88 |
| 11 | A Database of Narrative Schemas | 18 |
| 12 | Machine Translation Evaluation with Textual Entailment Features | 7 |
| 13 | Shallow Semantic Parsing using Support Vector Machines. | 264 |
| 14 | Semantic Role Labeling by Tagging Syntactic Chunks | 63 |
| 15 | Learning Syntactic Patterns for Automatic Hypernym Discovery | 415 |
| 16 | Identifying semantic relations in text | 0 |
| 17 | Building a Foundation System for Producing Short Answers to Factual Questions. | 6 |
| 18 | The Effects of Collocational Strength and Contextual Predictability in Lexical Production | 74 |
| 19 | 216 | |
| 20 | An on-line computational model of human sentence interpretation | 15 |
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.