Joachim Daiber
- Artificial Intelligence top 5%
- Information Systems top 10%
- Computer Vision and Pattern Recognition
- Management Science and Operations Research top 10%
- Molecular Biology
- Co-authors
- Pablo N. MendesMax JakobChris HokampYi LuShayne LongpreRob van der GootKhalil Sima’anStella Frank
- Topics
- Natural Language Processing Techniques (8 papers)Topic Modeling (6 papers)Semantic Web and Ontologies (2 papers)
- Journals
- Language Resources and EvaluationTransactions of the Association for Computational LinguisticsUvA-DARE (University of Amsterdam)
- Partner nations
- NetherlandsGermanyIsrael
In The Last Decade
Joachim Daiber
8 papers receiving 333 citations
Hit Papers
Peers
Comparison fields: 5 of 40
- Artificial Intelligence 327
- Information Systems 77
- Computer Vision and Pattern Recognition 55
- Management Science and Operations Research 48
- Molecular Biology 34
Countries citing papers authored by Joachim Daiber
This map shows the geographic impact of Joachim Daiber'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 Joachim Daiber with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joachim Daiber more than expected).
Fields of papers citing papers by Joachim Daiber
This network shows the impact of papers produced by Joachim Daiber. 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 Joachim Daiber. The network helps show where Joachim Daiber may publish in the future.
Co-authorship network of co-authors of Joachim Daiber
This figure shows the co-authorship network connecting the top 25 collaborators of Joachim Daiber. A scholar is included among the top collaborators of Joachim Daiber 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 Joachim Daiber. Joachim Daiber is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 51 | |
| 2 | The denoised web treebank: evaluating dependency parsing under noisy input conditions | 9 |
| 3 | Universal Reordering via Linguistic Typology | 4 |
| 4 | 3 | |
| 5 | Machine Translation with Source-Predicted Target Morphology | 3 |
| 6 | Splitting Compounds by Semantic Analogy | 8 |
| 7 | Improving efficiency and accuracy in multilingual entity extractionbreakdown → | 277 |
| 8 | Evaluating the Impact of Phrase Recognition on Concept Tagging | 3 |
About Joachim Daiber
Joachim Daiber is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 8 papers that have together received 358 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (8 papers), Topic Modeling (6 papers) and Semantic Web and Ontologies (2 papers). The work is most often cited by research in Artificial Intelligence (327 citations), Management Science and Operations Research (48 citations) and Information Systems (77 citations). Joachim Daiber has collaborated with scholars based in Netherlands, Germany and Israel. Frequent co-authors include Pablo N. Mendes, Max Jakob, Chris Hokamp, Yi Lu, Shayne Longpre, Rob van der Goot, Khalil Sima’an, Stella Frank, Miloš Stanojević and Christian Bizer. Their work appears in journals such as Language Resources and Evaluation, Transactions of the Association for Computational Linguistics and UvA-DARE (University of Amsterdam).
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.