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.
Farasa: A Fast and Furious Segmenter for Arabic
2016239 citationsAhmed Abdelalí, Kareem Darwish et al.profile →
Abusive Language Detection on Arabic Social Media
2017205 citationsHamdy Mubarak, Kareem Darwish et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Kareem Darwish
Since
Specialization
Citations
This map shows the geographic impact of Kareem Darwish'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 Kareem Darwish with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kareem Darwish more than expected).
This network shows the impact of papers produced by Kareem Darwish. 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 Kareem Darwish. The network helps show where Kareem Darwish may publish in the future.
Co-authorship network of co-authors of Kareem Darwish
This figure shows the co-authorship network connecting the top 25 collaborators of Kareem Darwish.
A scholar is included among the top collaborators of Kareem Darwish 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 Kareem Darwish. Kareem Darwish is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Eldesouki, Mohamed, et al.. (2019). FarSpeech: Arabic Natural Language Processing for Live Arabic Speech.. Conference of the International Speech Communication Association. 2372–2373.2 indexed citations
5.
Magdy, Walid, et al.. (2018). Part-of-Speech Tagging for Arabic Gulf Dialect Using Bi-LSTM. Language Resources and Evaluation. 3925–3932.11 indexed citations
6.
Darwish, Kareem & Hamdy Mubarak. (2016). Farasa: A New Fast and Accurate Arabic Word Segmenter. Language Resources and Evaluation. 1070–1074.39 indexed citations
7.
Eldesouki, Mohamed, Fahim Dalvi, Hassan Sajjad, & Kareem Darwish. (2016). QCRI $@$ DSL 2016: Spoken Arabic Dialect Identification Using Textual Features.. International Conference on Computational Linguistics. 221–226.14 indexed citations
Darwish, Kareem, Ahmed Abdelalí, & Hamdy Mubarak. (2014). Using Stem-Templates to Improve Arabic POS and Gender/Number Tagging. Language Resources and Evaluation. 2926–2931.23 indexed citations
10.
Darwish, Kareem, Ahmed Ali, & Ahmed Abdelalí. (2014). Query term expansion by automatic learning of morphological equivalence patterns from Wikipedia. International ACM SIGIR Conference on Research and Development in Information Retrieval. 24–29.1 indexed citations
11.
Darwish, Kareem & Wei Gao. (2014). Simple Effective Microblog Named Entity Recognition: Arabic as an Example. Language Resources and Evaluation. 2513–2517.15 indexed citations
12.
Mourad, Ahmed & Kareem Darwish. (2013). Subjectivity and Sentiment Analysis of Modern Standard Arabic and Arabic Microblogs. North American Chapter of the Association for Computational Linguistics. 55–64.115 indexed citations
13.
Sajjad, Hassan, Kareem Darwish, & Yonatan Belinkov. (2013). Translating Dialectal Arabic to English. Meeting of the Association for Computational Linguistics. 1–6.31 indexed citations
14.
Darwish, Kareem, et al.. (2012). Transliteration Mining Using Large Training and Test Sets. North American Chapter of the Association for Computational Linguistics. 243–252.5 indexed citations
15.
Fakhr, Mohamed Waleed, et al.. (2012). Statistical Denormalization for Arabic Text. Empirical Methods in Natural Language Processing. 228–232.7 indexed citations
16.
Darwish, Kareem, et al.. (2011). Improved Transliteration Mining Using Graph Reinforcement. Empirical Methods in Natural Language Processing. 1384–1393.18 indexed citations
17.
Darwish, Kareem, et al.. (2010). Simplified Feature Set for Arabic Named Entity Recognition. Meeting of the Association for Computational Linguistics. 110–115.52 indexed citations
18.
Darwish, Kareem, et al.. (2006). Building a Heterogeneous Information Retrieval Collection of Printed Arabic Documents.. Language Resources and Evaluation. 657–662.3 indexed citations
19.
Darwish, Kareem & Loutfy H. Madkour. (2004). The GUC Goes to TREC 2004: Using Whole or Partial Documents for Retrieval and Classification in the Genomics Track.. Text REtrieval Conference.6 indexed citations
20.
Darwish, Kareem, et al.. (2001). TREC-10 Experiments at University of Maryland CLIR and Video.. Text REtrieval Conference. 549–561.10 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.