Anoop Sarkar
About
In The Last Decade
Anoop Sarkar
89 papers receiving 1.8k citations
Peers
Comparison fields: 5 of 110
- Artificial Intelligence 1.8k
- Computer Vision and Pattern Recognition 333
- Molecular Biology 151
- Information Systems 144
- Experimental and Cognitive Psychology 129
Countries citing papers authored by Anoop Sarkar
This map shows the geographic impact of Anoop Sarkar'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 Anoop Sarkar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anoop Sarkar more than expected).
Fields of papers citing papers by Anoop Sarkar
This network shows the impact of papers produced by Anoop Sarkar. 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 Anoop Sarkar. The network helps show where Anoop Sarkar may publish in the future.
Co-authorship network of co-authors of Anoop Sarkar
This figure shows the co-authorship network connecting the top 25 collaborators of Anoop Sarkar. A scholar is included among the top collaborators of Anoop Sarkar 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 Anoop Sarkar. Anoop Sarkar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Simultaneous Translation using Optimized Segmentation | 6 |
| 2 | Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields | 6 |
| 3 | Learning segmentations that balance latency versus quality in spoken language translation. | 5 |
| 4 | Expressive hierarchical rule extraction for left-to-right translation | 3 |
| 5 | Multi-Metric Optimization Using Ensemble Tuning | 5 |
| 6 | Graph Propagation for Paraphrasing Out-of-Vocabulary Words in Statistical Machine Translation | 24 |
| 7 | Mixing Multiple Translation Models in Statistical Machine Translation | 19 |
| 8 | Improved Reordering for Shallow-n Grammar based Hierarchical Phrase-based Translation | 2 |
| 9 | Bootstrapping via Graph Propagation | 10 |
| 10 | Kriya - The SFU System for Translation Task at WMT-12 | 2 |
| 11 | Compact rule extraction for hierarchical phrase-based translation | 5 |
| 12 | An Ensemble Model that Combines Syntactic and Semantic Clustering for Discriminative Dependency Parsing | 14 |
| 13 | Incremental Decoding for Phrase-Based Statistical Machine Translation | 12 |
| 14 | Training a Perceptron with Global and Local Features for Chinese Word Segmentation | 1 |
| 15 | Experimental Evaluation of LTAG-Based Features for Semantic Role Labeling | 9 |
| 16 | Analysis of semi-supervised learning with the Yarowsky algorithm | 32 |
| 17 | Voting between Dictionary-Based and Subword Tagging Models for Chinese Word Segmentation | 1 |
| 18 | Discriminative Reranking for Machine Translation | 144 |
| 19 | Learning Verb Subcategorization from Corpora: Counting Frame Subsets. | 3 |
| 20 | The Conflict Between Future Tense and Modality: The Case of Will in English | 21 |
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.