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 →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Nadir Durrani'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 Nadir Durrani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nadir Durrani more than expected).
This network shows the impact of papers produced by Nadir Durrani. 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 Nadir Durrani. The network helps show where Nadir Durrani may publish in the future.
Co-authorship network of co-authors of Nadir Durrani
This figure shows the co-authorship network connecting the top 25 collaborators of Nadir Durrani.
A scholar is included among the top collaborators of Nadir Durrani 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 Nadir Durrani. Nadir Durrani is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Dalvi, Fahim, Nadir Durrani, Hassan Sajjad, Yonatan Belinkov, & Stephan Vogel. (2017). Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder. International Joint Conference on Natural Language Processing. 1. 142–151.26 indexed citations
9.
Belinkov, Yonatan, Lluı́s Màrquez, Hassan Sajjad, et al.. (2017). Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging Tasks. International Joint Conference on Natural Language Processing. 1. 1–10.39 indexed citations
10.
Durrani, Nadir, Hassan Sajjad, Shafiq Joty, & Ahmed Abdelalí. (2016). A Deep Fusion Model for Domain Adaptation in Phrase-based MT. International Conference on Computational Linguistics. 3177–3187.4 indexed citations
11.
Durrani, Nadir, Philipp Koehn, Helmut Schmid, & Alexander Fraser. (2014). Investigating the Usefulness of Generalized Word Representations in SMT. International Conference on Computational Linguistics. 421–432.20 indexed citations
12.
Durrani, Nadir, Barry Haddow, Philipp Koehn, & Kenneth Heafield. (2014). Edinburgh’s Phrase-based Machine Translation Systems for WMT-14. Workshop on Statistical Machine Translation. 97–104.1 indexed citations
13.
Fraser, Alexander, et al.. (2013). Munich-Edinburgh-Stuttgart Submissions at WMT13: Morphological and Syntactic Processing for SMT. Workshop on Statistical Machine Translation. 232–239.9 indexed citations
14.
Sajjad, Hassan, et al.. (2013). QCRI-MES Submission at WMT13: Using Transliteration Mining to Improve Statistical Machine Translation. Workshop on Statistical Machine Translation. 219–224.8 indexed citations
15.
Durrani, Nadir, Barry Haddow, Kenneth Heafield, & Philipp Koehn. (2013). Edinburgh's Machine Translation Systems for European Language Pairs. Workshop on Statistical Machine Translation. 114–121.26 indexed citations
16.
Durrani, Nadir, Alexander Fraser, Helmut Schmid, Hassan Sajjad, & Richárd Farkas. (2013). Munich-Edinburgh-Stuttgart Submissions of OSM Systems at WMT13. Workshop on Statistical Machine Translation. 122–127.7 indexed citations
17.
Durrani, Nadir, Alexander Fraser, & Helmut Schmid. (2013). Model With Minimal Translation Units, But Decode With Phrases. North American Chapter of the Association for Computational Linguistics. 1–11.24 indexed citations
18.
Durrani, Nadir, Alexander Fraser, Helmut Schmid, Hieu Hoang, & Philipp Koehn. (2013). Can Markov Models Over Minimal Translation Units Help Phrase-Based SMT?. Meeting of the Association for Computational Linguistics. 399–405.41 indexed citations
19.
Sajjad, Hassan, Nadir Durrani, Helmut Schmid, & Alexander Fraser. (2011). Comparing Two Techniques for Learning Transliteration Models Using a Parallel Corpus. International Joint Conference on Natural Language Processing. 129–137.6 indexed citations
20.
Durrani, Nadir & Sarmad Hussain. (2010). Urdu Word Segmentation. North American Chapter of the Association for Computational Linguistics. 528–536.52 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.