Mark A. Przybocki

Artificial Intelligence · 1997–2014

A researcher in the Science corpus: 7 papers; trusts 6 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 16 citations into it; #33,026 of 35,345 by trust here, #9,170 by citations
• Evaluation & Benchmarks — 15 citations into it; #1,910 of 3,147 by trust here, #1,706 by citations
• Natural Language Processing — 12 citations into it; #3,612 of 5,688 by trust here, #2,767 by citations

Whom Mark A. Przybocki trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Franz Joseph Och
2. Chris Callison-Burch
3. Philipp Koehn
4. Matthew G. Snover
5. Cameron S. Fordyce
6. Dekang Lin
7. J. Fleiss
8. Salim Roukos
9. Bonnie J. Dorr
10. Christof Monz

Who trusts Mark A. Przybocki overall

1. Juraj Juraska
2. Tom Kocmi
3. Chris Callison-Burch
4. Niko Brummer
5. Nizar Habash
6. André Anjos
7. Yvette Graham
8. Shamil Chollampatt
9. Panagiotis Karagiannis
10. Christian Federmann

Papers from Mark A. Przybocki's perspective

Ordered by how strongly the researchers Mark A. Przybocki trusts endorse each paper. Same corpus, a different order for every researcher.

1. Bleu: a Method for Automatic Evaluation of Machine Translation
2. Minimum Error Rate Training in Statistical Machine Translation
3. Automatic evaluation of machine translation quality using n-gram co-occurrence statistics
4. A Study of Translation Edit Rate with Targeted Human Annotation
5. METEOR: An Automatic Metric for MT Evaluation with High Levels of Correlation with Human Judgments
6. (Meta-) Evaluation of Machine Translation
7. Manual and Automatic Evaluation of Machine Translation between European Languages
8. Moses: Open Source Toolkit for Statistical Machine Translation
9. The measurement of observer agreement for categorical data.
10. A Coefficient of Agreement for Nominal Scales

Their papers

• An Introduction to Evaluating Biometric Systems (2000) — 462 citations
• The Automatic Content Extraction (ACE) Program – Tasks, Data, and Evaluation (2004) — 1,438 citations
• The NIST 2014 Speaker Recognition i-vector Machine Learning Challenge (2014) — 108 citations
• Findings of the 2010 Joint Workshop on Statistical Machine Translation and Metrics for Machine Translation (2010) — 233 citations
• The NIST 2008 Metrics for machine translation challenge—overview, methodology, metrics, and results (2009) — 71 citations
• The NIST speaker recognition evaluation - Overview, methodology, systems, results, perspective (2000) — 445 citations
• The DET curve in assessment of detection task performance (1997)