Nicola Cancedda
About
In The Last Decade
Nicola Cancedda
33 papers receiving 514 citations
Peers
Comparison fields: 5 of 43
- Artificial Intelligence 592
- Information Systems 94
- Computer Vision and Pattern Recognition 51
- Molecular Biology 40
- Computational Theory and Mathematics 19
Countries citing papers authored by Nicola Cancedda
This map shows the geographic impact of Nicola Cancedda'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 Nicola Cancedda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicola Cancedda more than expected).
Fields of papers citing papers by Nicola Cancedda
This network shows the impact of papers produced by Nicola Cancedda. 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 Nicola Cancedda. The network helps show where Nicola Cancedda may publish in the future.
Co-authorship network of co-authors of Nicola Cancedda
This figure shows the co-authorship network connecting the top 25 collaborators of Nicola Cancedda. A scholar is included among the top collaborators of Nicola Cancedda 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 Nicola Cancedda. Nicola Cancedda 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 | 1 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 4 | |
| 6 | Prediction of Learning Curves in Machine Translation | 21 |
| 7 | Private Access to Phrase Tables for Statistical Machine Translation | 5 |
| 8 | Productive Generation of Compound Words in Statistical Machine Translation | 11 |
| 9 | Confidence-Weighted Learning of Factored Discriminative Language Models | 1 |
| 10 | Intersecting Hierarchical and Phrase-Based Models of Translation: Formal Aspects and Algorithms | 2 |
| 11 | A Dataset for Assessing Machine Translation Evaluation Metrics | 22 |
| 12 | Minimum Error Rate Training by Sampling the Translation Lattice | 12 |
| 13 | Machine Translation Using Overlapping Alignments and SampleRank | 3 |
| 14 | 111 | |
| 15 | Kernel Methods for Document Filtering | 14 |
| 16 | 1 | |
| 17 | 11 | |
| 18 | 5 | |
| 19 | 4 | |
| 20 | 2 |
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.