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 Daniel Marcu'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 Daniel Marcu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Marcu more than expected).
This network shows the impact of papers produced by Daniel Marcu. 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 Daniel Marcu. The network helps show where Daniel Marcu may publish in the future.
Co-authorship network of co-authors of Daniel Marcu
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Marcu.
A scholar is included among the top collaborators of Daniel Marcu 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 Daniel Marcu. Daniel Marcu 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.
Tran, Ke, Yonatan Bisk, Ashish Vaswani, Daniel Marcu, & Kevin Knight. (2016). Unsupervised Neural Hidden Markov Models. UvA-DARE (University of Amsterdam). 63–71.25 indexed citations
2.
Choi, Eunsol, et al.. (2016). Extracting Structured Scholarly Information from the Machine Translation Literature. Language Resources and Evaluation. 421–425.3 indexed citations
3.
Bisk, Yonatan, Daniel Marcu, & William Wong. (2016). Towards a Dataset for Human Computer Communication via Grounded Language Acquisition. National Conference on Artificial Intelligence.12 indexed citations
4.
Riesa, Jason, Ann Irvine, & Daniel Marcu. (2011). Feature-Rich Language-Independent Syntax-Based Alignment for Statistical Machine Translation. Empirical Methods in Natural Language Processing. 497–507.16 indexed citations
5.
Riesa, Jason & Daniel Marcu. (2010). Hierarchical Search for Word Alignment. Meeting of the Association for Computational Linguistics. 157–166.22 indexed citations
6.
DeNeefe, Steve, Kevin Knight, Wei Wang, & Daniel Marcu. (2007). What Can Syntax-Based MT Learn from Phrase-Based MT?. Empirical Methods in Natural Language Processing. 755–763.62 indexed citations
7.
Wang, Wei, Kevin Knight, & Daniel Marcu. (2007). Binarizing Syntax Trees to Improve Syntax-Based Machine Translation Accuracy. Empirical Methods in Natural Language Processing. 746–754.54 indexed citations
8.
Fraser, Alexander & Daniel Marcu. (2007). Getting the Structure Right for Word Alignment: LEAF. Empirical Methods in Natural Language Processing. 51–60.44 indexed citations
9.
Narayanan, S. R., Sankaranarayanan Ananthakrishnan, Panayiotis Georgiou, et al.. (2004). The Transonics Spoken Dialogue Translator: An aid for English-Persian Doctor-Patient interviews. National Conference on Artificial Intelligence. 97–103.12 indexed citations
10.
Higgins, Derrick, Jill Burstein, Daniel Marcu, & Claudia Gentile. (2004). Evaluating Multiple Aspects of Coherence in Student Essays. North American Chapter of the Association for Computational Linguistics. 185–192.102 indexed citations
11.
Daumé, Hal & Daniel Marcu. (2004). A Phrase-Based HMM Approach to Document/Abstract Alignment. Empirical Methods in Natural Language Processing. 119–126.15 indexed citations
12.
Knight, Kevin, et al.. (2004). The ISI/USC MT system.. IWSLT. 59–60.7 indexed citations
13.
Echihabi, Abdessamad, Ulf Hermjakob, Eduard Hovy, et al.. (2003). Multiple-Engine Question Answering in TextMap.. Text REtrieval Conference. 772–781.28 indexed citations
14.
Hermjakob, Ulf, Abdessamad Echihabi, & Daniel Marcu. (2002). Natural Language Based Reformulation Resource and Wide Exploitation for Question Answering.. Text REtrieval Conference.37 indexed citations
15.
Burstein, Jill & Daniel Marcu. (2000). Benefits of Modularity in an Automated Essay Scoring System. International Conference on Computational Linguistics. 44–50.12 indexed citations
16.
Knight, Kevin & Daniel Marcu. (2000). Statistics-Based Summarization - Step One: Sentence Compression. National Conference on Artificial Intelligence. 703–710.269 indexed citations
17.
Marcu, Daniel, et al.. (2000). The automatic translation of discourse structures. The COCOON platform (University of Paris). 9–17.40 indexed citations
18.
Marcu, Daniel. (1998). Improving summarization through rhetorical parsing tuning. Meeting of the Association for Computational Linguistics.59 indexed citations
19.
Marcu, Daniel. (1997). From local to global coherence: a bottom-up approach to text planning. National Conference on Artificial Intelligence. 629–635.34 indexed citations
20.
Marcu, Daniel. (1996). Building up rhetorical structure trees. National Conference on Artificial Intelligence. 1069–1074.63 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.