Rion Snow
- Artificial Intelligence top 0.2%
- Computer Science Applications top 0.2%
- Information Systems top 1%
- Management Science and Operations Research top 1%
- Molecular Biology
- Co-authors
- Andrew Y. NgDaniel JurafskyDan JurafskyBrendan O’ConnorJimmy LinLucy VanderwendeArul MenezesSushant Prakash
- Topics
- Topic Modeling (9 papers)Natural Language Processing Techniques (8 papers)Speech and dialogue systems (3 papers)
- Cited by
- Computer Science ApplicationsArtificial IntelligenceManagement Science and Operations Research
- Journals
- Linear Algebra and its ApplicationsEmpirical Methods in Natural Language ProcessingNeural Information Processing Systems
- Partner nations
- United States
In The Last Decade
Rion Snow
10 papers receiving 3.5k citations
Hit Papers
Peers
Comparison fields: 5 of 110
- Artificial Intelligence 3.3k
- Computer Science Applications 783
- Information Systems 624
- Management Science and Operations Research 398
- Molecular Biology 365
Countries citing papers authored by Rion Snow
This map shows the geographic impact of Rion Snow'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 Rion Snow with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rion Snow more than expected).
Fields of papers citing papers by Rion Snow
This network shows the impact of papers produced by Rion Snow. 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 Rion Snow. The network helps show where Rion Snow may publish in the future.
Co-authorship network of co-authors of Rion Snow
This figure shows the co-authorship network connecting the top 25 collaborators of Rion Snow. A scholar is included among the top collaborators of Rion Snow 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 Rion Snow. Rion Snow is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 73 | |
| 2 | Distant supervision for relation extraction without labeled databreakdown → | 1622 |
| 3 | Learning Named Entity Hyponyms for Question Answering | 21 |
| 4 | Cheap and fast---but is it good?breakdown → | 1328 |
| 5 | Learning to Merge Word Senses | 55 |
| 6 | 40 | |
| 7 | 302 | |
| 8 | 14 | |
| 9 | Microsoft Research at RTE-2: Syntactic Contributions in the Entailment Task: an implementation | 23 |
| 10 | Learning Syntactic Patterns for Automatic Hypernym Discovery | 415 |
About Rion Snow
Rion Snow is a scholar working on Artificial Intelligence, Computer Science Applications and Industrial and Manufacturing Engineering, having authored 10 papers that have together received 3.9k indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Natural Language Processing Techniques (8 papers) and Speech and dialogue systems (3 papers). The work is most often cited by research in Computer Science Applications (783 citations), Artificial Intelligence (3.3k citations) and Management Science and Operations Research (398 citations). Rion Snow has collaborated with scholars based in United States. Frequent co-authors include Andrew Y. Ng, Daniel Jurafsky, Dan Jurafsky, Brendan O’Connor, Jimmy Lin, Lucy Vanderwende, Arul Menezes, Sushant Prakash, Patrick Schone and Nolan R. Wallach. Their work appears in journals such as Linear Algebra and its Applications, Empirical Methods in Natural Language Processing and Neural Information Processing Systems.
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.