Miriam Eckert
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Semantic Web and Ontologies
- Speech and dialogue systems
- Advanced Text Analysis Techniques
- Sentiment Analysis and Opinion Mining
- Language and Linguistics top 10%
Papers in
-
- Phonetics and Phonology Research 2
- Language, Metaphor, and Cognition 2
-
- Natural Language Processing Techniques 4
- Speech and dialogue systems 4
- Topic Modeling 1
- Sentiment Analysis and Opinion Mining 1
- Co-authors
- Michael Strube (2 shared papers)Michael Bada (3 shared papers)Lawrence Hunter (3 shared papers)William A. Baumgartner (2 shared papers)Karin M. Verspoor (2 shared papers)Kevin Bretonnel Cohen (2 shared papers)Donald L. Evans (1 shared paper)Judith A. Blake (1 shared paper)
- Journals
- BMC Bioinformatics (2 papers)Journal of Semantics (1 paper)Annual Review of Linguistics (1 paper)Computer Assisted Language Learning (1 paper)ERA (1 paper)
- Partner nations
- United StatesUnited KingdomAustralia
In The Last Decade
Miriam Eckert
8 papers receiving 392 citations
Peers
Comparison fields: 5 of 40
- Artificial Intelligence 291
- Language and Linguistics 47
- Molecular Biology 241
- Experimental and Cognitive Psychology 33
- Computational Theory and Mathematics 18
Countries citing papers authored by Miriam Eckert
This map shows the geographic impact of Miriam Eckert'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 Miriam Eckert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Miriam Eckert more than expected).
Fields of papers citing papers by Miriam Eckert
This network shows the impact of papers produced by Miriam Eckert. 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 Miriam Eckert. The network helps show where Miriam Eckert may publish in the future.
Co-authors
The 23 scholars most cited alongside Miriam Eckert, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2012 | 193 | |
| 2 | 2012 | 90 | |
| 3 | 2000 | 69 | |
| 4 | The ICWSM 2010 JDPA Sentiment Corpus for the Automotive Domain | 2010 | 25 |
| 5 | 1999 | 22 | |
| 6 | An Overview of the CRAFT Concept Annotation Guidelines | 2010 | 19 |
| 7 | 1994 | 13 | |
| 8 | Discourse deixis and null anaphora in German | 1998 | 3 |
| 9 | 1993 | 1 | |
| 10 | 2025 | 0 |
About Miriam Eckert
Miriam Eckert is a scholar working on Experimental and Cognitive Psychology, Artificial Intelligence, Language and Linguistics, Molecular Biology and Insect Science, having authored 10 papers that have together received 435 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (4 papers), Speech and dialogue systems (4 papers), Biomedical Text Mining and Ontologies (3 papers), Phonetics and Phonology Research (2 papers), Language, Metaphor, and Cognition (2 papers), Linguistic research and analysis (1 paper), Topic Modeling (1 paper) and Sentiment Analysis and Opinion Mining (1 paper). The work is most often cited by research in Artificial Intelligence (291 citations), Language and Linguistics (47 citations), Molecular Biology (241 citations), Experimental and Cognitive Psychology (33 citations) and Computational Theory and Mathematics (18 citations). Miriam Eckert has collaborated with scholars based in United States, United Kingdom and Australia. Frequent co-authors include Michael Strube, Michael Bada, Lawrence Hunter, William A. Baumgartner, Karin M. Verspoor, Kevin Bretonnel Cohen, Donald L. Evans, Judith A. Blake, Dmitry M. Sitnikov and Kristin Garcia. Their work appears in journals such as BMC Bioinformatics, Journal of Semantics, Annual Review of Linguistics, Computer Assisted Language Learning and ERA.
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.