Michael Bloodgood

603 total citations
18 papers, 202 citations indexed

About

Michael Bloodgood is a scholar working on Artificial Intelligence, Language and Linguistics and Molecular Biology. According to data from OpenAlex, Michael Bloodgood has authored 18 papers receiving a total of 202 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 2 papers in Language and Linguistics and 1 paper in Molecular Biology. Recurrent topics in Michael Bloodgood's work include Natural Language Processing Techniques (12 papers), Topic Modeling (10 papers) and Machine Learning and Algorithms (6 papers). Michael Bloodgood is often cited by papers focused on Natural Language Processing Techniques (12 papers), Topic Modeling (10 papers) and Machine Learning and Algorithms (6 papers). Michael Bloodgood collaborates with scholars based in United States. Michael Bloodgood's co-authors include K. Vijay‐Shanker, Chris Callison-Burch, Lori Levin, Christine Piatko, Bonnie J. Dorr, Nathaniel Wesley Filardo, Benjamin Strauss, S.L. Miller, Owen Rambow and Vinodkumar Prabhakaran and has published in prestigious journals such as The Journal of Cell Biology, Computational Linguistics and arXiv (Cornell University).

In The Last Decade

Michael Bloodgood

18 papers receiving 179 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Michael Bloodgood United States 9 181 17 15 9 9 18 202
Lili Kotlerman Israel 8 214 1.2× 15 0.9× 24 1.6× 7 0.8× 3 0.3× 15 226
Daisuke Bekki Japan 8 134 0.7× 25 1.5× 5 0.3× 9 1.0× 4 0.4× 27 146
Ai Ti Aw Singapore 8 206 1.1× 27 1.6× 15 1.0× 5 0.6× 2 0.2× 20 217
Natalie Schluter Denmark 9 236 1.3× 22 1.3× 19 1.3× 9 1.0× 2 0.2× 24 256
Felix Hieber Germany 7 118 0.7× 31 1.8× 6 0.4× 8 0.9× 11 1.2× 9 124
Alexey Sorokin Russia 6 160 0.9× 17 1.0× 13 0.9× 8 0.9× 2 0.2× 17 184
Bogdan Sacaleanu Germany 6 139 0.8× 25 1.5× 41 2.7× 5 0.6× 4 0.4× 17 154
Simona Gandrabur Canada 6 342 1.9× 25 1.5× 15 1.0× 7 0.8× 2 0.2× 7 349
Sergio Ortiz-Rojas Spain 8 300 1.7× 35 2.1× 9 0.6× 35 3.9× 3 0.3× 13 319
Altaf Rahman United States 9 270 1.5× 21 1.2× 31 2.1× 5 0.6× 2 0.2× 11 283

Countries citing papers authored by Michael Bloodgood

Since Specialization
Citations

This map shows the geographic impact of Michael Bloodgood'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 Michael Bloodgood with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Bloodgood more than expected).

Fields of papers citing papers by Michael Bloodgood

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Bloodgood. 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 Michael Bloodgood. The network helps show where Michael Bloodgood may publish in the future.

Co-authorship network of co-authors of Michael Bloodgood

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Bloodgood. A scholar is included among the top collaborators of Michael Bloodgood 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 Michael Bloodgood. Michael Bloodgood is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Strauss, Benjamin, et al.. (2016). Tissue homogeneity requires inhibition of unequal gene silencing during development. The Journal of Cell Biology. 214(3). 319–331. 5 indexed citations
2.
Bloodgood, Michael, et al.. (2015). Analysis of Stopping Active Learning based on Stabilizing Predictions. arXiv (Cornell University). 10–19. 6 indexed citations
3.
Prabhakaran, Vinodkumar, Michael Bloodgood, Mona Diab, et al.. (2015). Statistical modality tagging from rule-based annotations and crowdsourcing. arXiv (Cornell University). 10 indexed citations
4.
Bloodgood, Michael & Chris Callison-Burch. (2014). Using Mechanical Turk to Build Machine Translation Evaluation Sets. arXiv (Cornell University). 208–211. 10 indexed citations
5.
Bloodgood, Michael, Peng Ye, Paul Rodrigues, David Zajic, & David Doermann. (2014). A random forest system combination approach for error detection in digital dictionaries. arXiv (Cornell University). 78–86. 1 indexed citations
6.
Bloodgood, Michael & Benjamin Strauss. (2014). Translation memory retrieval methods. 202–210. 14 indexed citations
7.
Bloodgood, Michael & Chris Callison-Burch. (2014). Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation. arXiv (Cornell University). 854–864. 27 indexed citations
8.
Bloodgood, Michael, et al.. (2014). A Modality Lexicon and its use in Automatic Tagging. arXiv (Cornell University). 18 indexed citations
9.
Bloodgood, Michael, Bonnie J. Dorr, Chris Callison-Burch, et al.. (2012). Modality and Negation in SIMT Use of Modality and Negation in Semantically-Informed Syntactic MT. Computational Linguistics. 38(2). 411–438. 33 indexed citations
10.
Rodrigues, Paul, David Zajic, David Doermann, Michael Bloodgood, & Peng Ye. (2011). Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling. Digital Repository at the University of Maryland (University of Maryland College Park). 227–232. 2 indexed citations
11.
Bloodgood, Michael, Chris Callison-Burch, Bonnie J. Dorr, et al.. (2010). Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach. Digital Repository at the University of Maryland (University of Maryland College Park). 4 indexed citations
12.
Bloodgood, Michael & K. Vijay‐Shanker. (2009). A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping. Digital Repository at the University of Maryland (University of Maryland College Park). 39–47. 33 indexed citations
13.
Bloodgood, Michael, et al.. (2009). Taking into account the differences between actively and passively acquired data. 9 indexed citations
14.
Shanker, Vijay & Michael Bloodgood. (2009). Active learning with support vector machines for imbalanced datasets and a method for stopping active learning based on stabilizing predictions. 1 indexed citations
15.
Bloodgood, Michael & K. Vijay‐Shanker. (2009). Taking into Account the Differences between Actively and Passively Acquired Data: The Case of Active Learning with Support Vector Machines for Imbalanced Datasets. Digital Repository at the University of Maryland (University of Maryland College Park). 137–140. 24 indexed citations
16.
Bloodgood, Michael & K. Vijay‐Shanker. (2008). An Approach to Reducing Annotation Costs for BioNLP. Digital Repository at the University of Maryland (University of Maryland College Park). 104–105. 1 indexed citations
17.
Miller, John E., Michael Bloodgood, Manabu Torii, & K. Vijay‐Shanker. (2006). Rapid adaptation of POS tagging for domain specific uses. 118–118. 2 indexed citations
18.
Miller, John E., Michael Bloodgood, Manabu Torii, & K. Vijay‐Shanker. (2006). Rapid Adaptation of POS Tagging for Domain Specific Uses. Digital Repository at the University of Maryland (University of Maryland College Park). 118–119. 2 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.

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