Gabriella Lapesa
- Artificial Intelligence top 10%
- Language and Linguistics top 10%
- Communication
- General Social Sciences top 5%
- Cultural Studies top 10%
- Co-authors
- Stefan EvertSebastian PadóAlessandro LenciSabine Schulte im WaldeJonas KuhnSebastian HaunssIngo PlagAntje Roßdeutscher
- Topics
- Topic Modeling (18 papers)Natural Language Processing Techniques (12 papers)Social Media and Politics (5 papers)
In The Last Decade
Gabriella Lapesa
24 papers receiving 179 citations
Peers
Comparison fields: 5 of 33
- Artificial Intelligence 160
- Language and Linguistics 30
- Communication 19
- General Social Sciences 17
- Cultural Studies 14
Countries citing papers authored by Gabriella Lapesa
This map shows the geographic impact of Gabriella Lapesa'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 Gabriella Lapesa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gabriella Lapesa more than expected).
Fields of papers citing papers by Gabriella Lapesa
This network shows the impact of papers produced by Gabriella Lapesa. 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 Gabriella Lapesa. The network helps show where Gabriella Lapesa may publish in the future.
Co-authorship network of co-authors of Gabriella Lapesa
This figure shows the co-authorship network connecting the top 25 collaborators of Gabriella Lapesa. A scholar is included among the top collaborators of Gabriella Lapesa 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 Gabriella Lapesa. Gabriella Lapesa is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 5 | |
| 6 | 4 | |
| 7 | Regression Analysis of Lexical and Morpho-Syntactic Properties of Kiezdeutsch | 1 |
| 8 | 11 | |
| 9 | 6 | |
| 10 | DEbateNet-mig15:Tracing the 2015 Immigration Debate in Germany Over Time | 7 |
| 11 | 3 | |
| 12 | 14 | |
| 13 | 4 | |
| 14 | Modeling Derivational Morphology in Ukrainian. | 1 |
| 15 | Are doggies really nicer than dogs? The impact of morphological derivation on emotional valence in German. | 3 |
| 16 | 21 | |
| 17 | 47 | |
| 18 | Evaluating Neighbor Rank and Distance Measures as Predictors of Semantic Priming | 18 |
| 19 | LexIt: A Computational Resource on Italian Argument Structure | 13 |
| 20 | Building an Italian FrameNet through semi-automatic corpus analysis | 8 |
About Gabriella Lapesa
Gabriella Lapesa is a scholar working on General Social Sciences, Communication and Artificial Intelligence, having authored 29 papers that have together received 198 indexed citations. Recurring topics across this work include Topic Modeling (18 papers), Natural Language Processing Techniques (12 papers) and Social Media and Politics (5 papers). The work is most often cited by research in General Social Sciences (17 citations), Artificial Intelligence (160 citations) and Language and Linguistics (30 citations). Gabriella Lapesa has collaborated with scholars based in Germany, Italy and France. Frequent co-authors include Stefan Evert, Sebastian Padó, Alessandro Lenci, Sabine Schulte im Walde, Jonas Kuhn, Sebastian Haunss, Ingo Plag, Antje Roßdeutscher, Serena Villata and Dominik Schlechtweg. Their work appears in journals such as Language Resources and Evaluation, Transactions of the Association for Computational Linguistics and Politics and Governance.
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.