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.
SemEval-2018 Task 1: Affect in Tweets
2018463 citationsSaif M. Mohammad, Felipe Bravo-Márquez et al.NPARCprofile →
Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions
202471 citationsMohammad Salameh, Randy Goebel 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 Mohammad Salameh
Since
Specialization
Citations
This map shows the geographic impact of Mohammad Salameh'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 Mohammad Salameh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohammad Salameh more than expected).
Fields of papers citing papers by Mohammad Salameh
This network shows the impact of papers produced by Mohammad Salameh. 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 Mohammad Salameh. The network helps show where Mohammad Salameh may publish in the future.
Co-authorship network of co-authors of Mohammad Salameh
This figure shows the co-authorship network connecting the top 25 collaborators of Mohammad Salameh.
A scholar is included among the top collaborators of Mohammad Salameh 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 Mohammad Salameh. Mohammad Salameh is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Salameh, Mohammad, Houda Bouamor, & Nizar Habash. (2018). Fine-Grained Arabic Dialect Identification. International Conference on Computational Linguistics. 1332–1344.61 indexed citations
9.
Bouamor, Houda, Nizar Habash, Mohammad Salameh, et al.. (2018). The madar Arabic dialect corpus and lexicon. Language Resources and Evaluation. 3387–3396.106 indexed citations
10.
Mohammad, Saif M., Felipe Bravo-Márquez, Mohammad Salameh, & Svetlana Kiritchenko. (2018). SemEval-2018 Task 1: Affect in Tweets. NPARC. 1–17.463 indexed citations breakdown →
11.
Mohammad, Saif M., Mohammad Salameh, & Svetlana Kiritchenko. (2016). Sentiment lexicons for Arabic social media. Language Resources and Evaluation. 33–37.39 indexed citations
12.
Mohammad, Saif M., Mohammad Salameh, & Svetlana Kiritchenko. (2016). How Translation Alters Sentiment. Journal of Artificial Intelligence Research. 55. 95–130.122 indexed citations
Nicolai, Garrett, Bradley Hauer, Mohammad Salameh, Lei Yao, & Grzegorz Kondrak. (2013). Cognate and Misspelling Features for Natural Language Identification. 140–145.10 indexed citations
18.
Salameh, Mohammad, Colin Cherry, & Grzegorz Kondrak. (2013). Reversing Morphological Tokenization in English-to-Arabic SMT. North American Chapter of the Association for Computational Linguistics. 47–53.2 indexed citations
19.
Kondrak, Grzegorz, Xingkai Li, & Mohammad Salameh. (2012). Transliteration Experiments on Chinese and Arabic. Meeting of the Association for Computational Linguistics. 71–75.3 indexed citations
20.
Salameh, Mohammad, Rached Zantout, & Nashat Mansour. (2011). Improving the Accuracy of English-Arabic Statistical Sentence Alignment. The International Arab Journal of Information Technology. 8. 171–177.3 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.