This map shows the geographic impact of Leo Wanner'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 Leo Wanner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Leo Wanner more than expected).
This network shows the impact of papers produced by Leo Wanner. 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 Leo Wanner. The network helps show where Leo Wanner may publish in the future.
Co-authorship network of co-authors of Leo Wanner
This figure shows the co-authorship network connecting the top 25 collaborators of Leo Wanner.
A scholar is included among the top collaborators of Leo Wanner 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 Leo Wanner. Leo Wanner is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Wanner, Leo, et al.. (2020). ThemePro: A Toolkit for the Analysis of Thematic Progression. Language Resources and Evaluation. 1000–1007.1 indexed citations
Farrús, Mireia, et al.. (2017). Prosograph: a tool for prosody visualisation of large speech corpora. Repositori digital de la UPF (Universitat Pompeu Fabra). 809–810.6 indexed citations
6.
Wanner, Leo. (2015). Multiple Language Gender Identification for Blog Posts.. Cognitive Science.5 indexed citations
7.
Wanner, Leo, et al.. (2014). How to Use less Features and Reach Better Performance in Author Gender Identification. Language Resources and Evaluation. 1315–1319.12 indexed citations
8.
Johansson, Lasse, Ari Karppinen, & Leo Wanner. (2013). The fusion of meteorological- and air quality information for orchestrated services using environmental profiling. International Conference on Information Fusion. 1638–1644.
9.
Bouayad‐Agha, Nadjet, et al.. (2013). Overview of the First Content Selection Challenge from Open Semantic Web Data. 98–102.2 indexed citations
10.
Bohnet, Bernd, et al.. (2013). Towards the Annotation of Penn TreeBank with Information Structure. International Joint Conference on Natural Language Processing. 1250–1256.9 indexed citations
11.
Wanner, Leo, Simon Mille, & Bernd Bohnet. (2012). Towards a Surface Realization-Oriented Corpus Annotation. 22–30.4 indexed citations
12.
Mille, Simon, et al.. (2012). How Does the Granularity of an Annotation Scheme Influence Dependency Parsing Performance. International Conference on Computational Linguistics. 839–852.9 indexed citations
13.
Vivaldi, Jorge, et al.. (2012). Co-occurrence graphs applied to taxonomy extraction in scientific and technical corpora. Procesamiento del lenguaje natural. 49(49). 67–74.4 indexed citations
14.
Bouayad‐Agha, Nadjet, et al.. (2011). Content selection from an ontology-based knowledge base for the generation of football summaries. 72–81.17 indexed citations
15.
Bouayad‐Agha, Nadjet, et al.. (2009). Simplification of Patent Claim Sentences for Their Paraphrasing and Summarization. The Florida AI Research Society.9 indexed citations
16.
Ramos, Margarita Alonso, Owen Rambow, & Leo Wanner. (2008). Using Semantically Annotated Corpora to Build Collocation Resources. Language Resources and Evaluation.6 indexed citations
17.
Karppinen, Ari, Pilvi Siljamo, Jaakko Kukkonen, et al.. (2007). Pollen: A Challenge for Environmental Information Services. 75–79.1 indexed citations
18.
Koch, Steffen, Ioannis Kompatsiaris, Symeon Papadopoulos, et al.. (2007). A Modular Framework for Ontology-based Representation of Patent Information. 49–58.15 indexed citations
19.
Wanner, Leo & Margarita Alonso Ramos. (2006). Local Document Relevance Clustering in IR Using Collocation Information.. Language Resources and Evaluation. 1886–1889.
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.