Sanja Štajner
About
In The Last Decade
Sanja Štajner
56 papers receiving 793 citations
Peers
Comparison fields: 5 of 51
- Artificial Intelligence 797
- General Health Professions 45
- Information Systems 31
- Human Factors and Ergonomics 30
- Developmental and Educational Psychology 23
Countries citing papers authored by Sanja Štajner
This map shows the geographic impact of Sanja Štajner'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 Sanja Štajner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sanja Štajner more than expected).
Fields of papers citing papers by Sanja Štajner
This network shows the impact of papers produced by Sanja Štajner. 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 Sanja Štajner. The network helps show where Sanja Štajner may publish in the future.
Co-authorship network of co-authors of Sanja Štajner
This figure shows the co-authorship network connecting the top 25 collaborators of Sanja Štajner. A scholar is included among the top collaborators of Sanja Štajner 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 Sanja Štajner. Sanja Štajner is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 17 | |
| 2 | When shallow is good enough: Automatic assessment of conceptual text complexity using shallow semantic features | 3 |
| 3 | CoCo: A Tool for Automatically Assessing Conceptual Complexity of Texts. | 5 |
| 4 | Automatic Assessment of Conceptual Text Complexity Using Knowledge Graphs | 4 |
| 5 | Data-Driven Text Simplification | 8 |
| 6 | A Detailed Evaluation of Neural Sequence-to-Sequence Models for In-domain and Cross-domain Text Simplification. | 11 |
| 7 | CWIG3G2 - Complex Word Identification Task across Three Text Genres and Two User Groups | 26 |
| 8 | 118 | |
| 9 | 21 | |
| 10 | Use of Domain-Specific Language Resources in Machine Translation | 2 |
| 11 | Bootstrapping a Hybrid MT System to a New Language Pair | 1 |
| 12 | Translating from original to simplified sentences using Moses: when does it actually work? | 1 |
| 13 | Automatic text simplification for Spanish: comparative evaluation of various simplification strategies | 20 |
| 14 | Translating sentences from ‘original’ to ‘simplified’ Spanish | 8 |
| 15 | Eliminación de frases y decisiones de división basadas en corpus para simplificación de textos en español | 2 |
| 16 | 15 | |
| 17 | Adapting Text Simplification Decisions to Different Text Genres and Target Users | 2 |
| 18 | Readability Indices for Automatic Evaluation of Text Simplification Systems: A Feasibility Study for Spanish | 9 |
| 19 | Event-Centered Simplification of News Stories | 10 |
| 20 | Diachronic Changes in Text Complexity in 20th Century English Language: An NLP Approach | 2 |
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.