Automated Author ProfileDreyer, Markus
Dreyer, Markus
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.9 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Introduction
HyTER Networks of Selected OpenMT08/09 Progress Set Sentences was developed by SDL and contains HyTER (Hybrid Translation Edit Rate) networks for 102 selected source Arabic and Chinese sentences from OpenMT08 and OpenMT09 Progress Set data. HyTER is an evaluation metric based on large reference networks created by an annotation tool that allows users to develop an exponential number of correct translations for a given sentence. Reference networks can be used as a foundation for developing improved machine translation evaluation metrics and for automating the evaluation of human translation efficiency.
Data
The source material is comprised of Arabic and Chinese newswire and web data collected by LDC in 2007. Annotators created meaning-equivalent annotations under three annotation protocols. In the first protocol, foreign language native speakers built English networks starting from foreign language sentences. In the second, English native speakers built English networks from the best translation of a foreign language sentence as identified by NIST (National Institute of Standards and Technology). In the third protocol, English native speakers built English networks starting from the best translation, but those annotators also had access to three additional, independently produced human translations. Networks created by different annotators for each sentence were combined and evaluated.
This release includes the source sentences and four human reference translations produced by LDC in XML format, along with five machine translation system outputs representing a variety of system architectures and performance, and the human post-edited output of those systems also presented in XML.
Samples
Please view this FST sample and Reference XML sample.
Updates
None at this time.
Portions © 2007 Agence France Presse, Al-Ahram, Al Hayat, Al-Quds Al-Arabi, Asharq Al-Awsat, An Nahar, Assabah, China Military Online,Chinanews.com, Guangming Daily, Xinhua News Agency, © 2007, 2013, 2014 Trustees of the University of Pennsylvania
Authors
- Dreyer, Markus ;
- Marcu, Daniel