Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A Survey on Hate Speech Detection using Natural Language Processing
2017727 citationsAnna Grau Schmidt, Michael Wiegandprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Michael Wiegand
Since
Specialization
Citations
This map shows the geographic impact of Michael Wiegand'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 Michael Wiegand with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Wiegand more than expected).
This network shows the impact of papers produced by Michael Wiegand. 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 Michael Wiegand. The network helps show where Michael Wiegand may publish in the future.
Co-authorship network of co-authors of Michael Wiegand
This figure shows the co-authorship network connecting the top 25 collaborators of Michael Wiegand.
A scholar is included among the top collaborators of Michael Wiegand 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 Michael Wiegand. Michael Wiegand is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Berg, Esther van den, et al.. (2020). Doctor Who? Framing Through Names and Titles in German.. Language Resources and Evaluation. 4924–4932.1 indexed citations
2.
Wiegand, Michael, et al.. (2019). A Supervised Learning Approach for the Extraction of Sources and Targets from German Text..1 indexed citations
3.
Wiegand, Michael, et al.. (2018). Disambiguation of verbal shifters. Language Resources and Evaluation. 608–612.1 indexed citations
4.
Ruppenhofer, Josef, et al.. (2018). Distinguishing affixoid formations from compounds. Publication Server of the Institute for German Language (Institute for German Language). 3853–3865.1 indexed citations
5.
Schmidt, Anna Grau & Michael Wiegand. (2017). A Survey on Hate Speech Detection using Natural Language Processing. 1–10.727 indexed citations breakdown →
6.
Ruppenhofer, Josef, et al.. (2015). Ordering adverbs by their scaling effect on adjective intensity. ERef Bayreuth (University of Bayreuth). 545–554.4 indexed citations
7.
Wiegand, Michael & Dietrich Klakow. (2014). Separating Brands from Types: an Investigation of Different Features for the Food Domain. Publication Server of the Institute for German Language (Institute for German Language). 2291–2302.1 indexed citations
8.
Wiegand, Michael, et al.. (2012). A Gold Standard for Relation Extraction in the Food Domain. Language Resources and Evaluation. 507–514.9 indexed citations
9.
Roth, Benjamin, et al.. (2012). Generalizing from Freebase and Patterns using Cluster-Based Distant Supervision for TAC KBP Slotfilling 2012.. Theory and applications of categories.6 indexed citations
10.
Wiegand, Michael & Dietrich Klakow. (2012). Generalization Methods for In-Domain and Cross-Domain Opinion Holder Extraction. Publication Server of the Institute for German Language (Institute for German Language). 325–335.10 indexed citations
Wiegand, Michael & Dietrich Klakow. (2011). Prototypical Opinion Holders: What We can Learn from Experts and Analysts. Publication Server of the Institute for German Language (Institute for German Language). 282–288.3 indexed citations
13.
Xu, Fang, et al.. (2011). Saarland University Spoken Language Systems Group at TAC KBP 2011. Theory and applications of categories.1 indexed citations
14.
Wiegand, Michael, Alexandra Balahur, Benjamin Roth, Dietrich Klakow, & Andrés Montoyo. (2010). A survey on the role of negation in sentiment analysis. Publication Server of the Institute for German Language (Institute for German Language). 60–68.145 indexed citations
15.
Momtazi, Saeedeh, Michael Wiegand, Fang Xu, et al.. (2010). Saarland University Spoken Language Systems at the Slot Filling Task of TAC KBP 2010. Theory and applications of categories.6 indexed citations
16.
Wiegand, Michael & Dietrich Klakow. (2010). Convolution Kernels for Opinion Holder Extraction. Publication Server of the Institute for German Language (Institute for German Language). 795–803.27 indexed citations
17.
Wiegand, Michael & Dietrich Klakow. (2009). The Role of Knowledge-based Features in Polarity Classification at Sentence Level. Publication Server of the Institute for German Language (Institute for German Language).6 indexed citations
Wiegand, Michael, et al.. (2008). The Alyssa System at TAC QA 2008. Theory and applications of categories.2 indexed citations
20.
Shen, Dan, et al.. (2007). The Alyssa System at TREC QA 2007: Do We Need Blog06?. Text REtrieval Conference.11 indexed citations
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.