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.
This map shows the geographic impact of Dekai Wu'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 Dekai Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dekai Wu more than expected).
This network shows the impact of papers produced by Dekai Wu. 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 Dekai Wu. The network helps show where Dekai Wu may publish in the future.
Co-authorship network of co-authors of Dekai Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Dekai Wu.
A scholar is included among the top collaborators of Dekai Wu 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 Dekai Wu. Dekai Wu 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.
Wu, Dekai, et al.. (2015). Learning to rap battle with bilingual recursive neural networks. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 2524–2530.2 indexed citations
2.
Lo, Chi-kiu, et al.. (2013). Improving machine translation by training against an automatic semantic frame based evaluation metric. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 375–381.14 indexed citations
3.
Wu, Dekai, et al.. (2013). Bayesian Induction of Bracketing Inversion Transduction Grammars. International Joint Conference on Natural Language Processing. 1158–1166.1 indexed citations
4.
Lo, Chi-kiu, et al.. (2012). Accuracy and robustness in measuring the lexical similarity of semantic role fillers for automatic semantic MT evaluation. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 574–581.7 indexed citations
5.
Lo, Chi-kiu & Dekai Wu. (2011). MEANT: An inexpensive, high-accuracy, semi-automatic metric for evaluating translation utility based on semantic roles. Meeting of the Association for Computational Linguistics. 1. 220–229.55 indexed citations
6.
Lo, Chi-kiu & Dekai Wu. (2011). Structured vs. Flat Semantic Role Representations for Machine Translation Evaluation. Meeting of the Association for Computational Linguistics. 10–20.11 indexed citations
Nivre, Joakim, et al.. (2010). Linear Inversion Transduction Grammar Alignments as a Second Translation Path. Workshop on Statistical Machine Translation. 167–171.1 indexed citations
9.
Lo, Chi-kiu & Dekai Wu. (2010). Evaluating Machine Translation Utility via Semantic Role Labels. Language Resources and Evaluation.8 indexed citations
10.
Wu, Dekai & David Chiang. (2009). Proceedings of the Third Workshop on Syntax and Structure in Statistical Translation (SSST-3) at NAACL HLT 2009. North American Chapter of the Association for Computational Linguistics.2 indexed citations
11.
Wu, Dekai & David Chiang. (2008). Proceedings of SSST-2, Second Workshop on Syntax and Structure in Statistical Translation. Meeting of the Association for Computational Linguistics.1 indexed citations
12.
Carpuat, Marine & Dekai Wu. (2007). Improving Statistical Machine Translation Using Word Sense Disambiguation. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 61–72.230 indexed citations
13.
Carpuat, Marine & Dekai Wu. (2005). Evaluating the Word Sense Disambiguation Performance of Statistical Machine Translation. International Joint Conference on Natural Language Processing.18 indexed citations
14.
Carpuat, Marine, Weifeng Su, & Dekai Wu. (2004). Augmenting Ensemble Classification for Word Sense Disambiguation with a Kernel PCA Model. Meeting of the Association for Computational Linguistics. 88–92.13 indexed citations
15.
Wu, Dekai, et al.. (1999). Automatically Merging Lexicons that have Incompatible Part-of-Speech Categories. Empirical Methods in Natural Language Processing.3 indexed citations
16.
Wu, Dekai, et al.. (1995). Using Brackets to Improve Search for Statistical Machine Translation. Pacific Asia Conference on Language, Information, and Computation. 195–204.2 indexed citations
17.
Wu, Dekai. (1995). Stochastic inversion transduction grammars with application to segmentation, bracketing, and alignment of parallel corpora. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1328–1335.49 indexed citations
18.
Wu, Dekai. (1994). Review of Statistically-driven computer grammars of English: the IBM/lancaster approach by Ezra Black, Roger Garside, and Geoffrey Leech. Editions Rodopi 1993.. Computational Linguistics. 20(3). 498–500.15 indexed citations
19.
Wu, Dekai. (1993). Estimating probability distributions over hypotheses with variable unification. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 790–795.1 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.