Roland Kühn

72 papers receiving 1.8k citations

Peers

Roland Kühn
Comparison fields: 5 of 83
  • Artificial Intelligence 2.0k
  • Signal Processing 596
  • Computer Vision and Pattern Recognition 244
  • Information Systems 108
  • Molecular Biology 89
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Countries citing papers authored by Roland Kühn

Since Specialization
Citations

This map shows the geographic impact of Roland Kühn'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 Roland Kühn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Roland Kühn more than expected).

Fields of papers citing papers by Roland Kühn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Roland Kühn. 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 Roland Kühn. The network helps show where Roland Kühn may publish in the future.

Co-authorship network of co-authors of Roland Kühn

This figure shows the co-authorship network connecting the top 25 collaborators of Roland Kühn. A scholar is included among the top collaborators of Roland Kühn 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 Roland Kühn. Roland Kühn is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 5
3
Indigenous language technologies in Canada: Assessment, challenges, and successes
19
4
Bilingual Methods for Adaptive Training Data Selection for Machine Translation
16
5
A Comparison of Mixture and Vector Space Techniques for Translation Model Adaptation
3
6
Coarse "split and lump" bilingual language models for richer source information in SMT.
8
7
Adaptation of Reordering Models for Statistical Machine Translation
13
8
PORT: a Precision-Order-Recall MT Evaluation Metric for Tuning
20
9
Enlarging Paraphrase Collections through Generalization and Instantiation
16
10
Improving AMBER, an MT Evaluation Metric
14
11
AMBER: A Modified BLEU, Enhanced Ranking Metric
20
12
Semantic smoothing and fabrication of phrase pairs for SMT
3
13
Bilingual Sense Similarity for Statistical Machine Translation
8
14
Translating Structured Documents
11
15
Lessons from NRC's Portage System at WMT 2010
16
16
Discriminative Instance Weighting for Domain Adaptation in Statistical Machine Translation
134
17
Phrase Clustering for Smoothing TM Probabilities - or, How to Extract Paraphrases from Phrase Tables
10
18
Improving Translation Quality by Discarding Most of the Phrasetable
140
19
Existence : crise et création
4
20
Corrections to A Cache-Based Language Model for Speech Recognition
16

About Roland Kühn

Roland Kühn is a scholar working on Artificial Intelligence, Signal Processing and Hardware and Architecture, having authored 75 papers that have together received 2.1k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (51 papers), Topic Modeling (39 papers) and Speech Recognition and Synthesis (20 papers). The work is most often cited by research in Artificial Intelligence (2.0k citations), Signal Processing (596 citations) and Computer Vision and Pattern Recognition (244 citations). Roland Kühn has collaborated with scholars based in Canada, United States and Germany. Frequent co-authors include George Foster, Renato De Mori, Nancy Niedzielski, J.-C. Junqua, P. Nguyen, Howard Johnson, Boxing Chen, Cyril Goutte, Joel Martin and Pierre Isabelle. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Speech and Audio Processing and Language Resources and Evaluation.

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.

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