Mark Carman

2.9k citations
79 papers · 1.6k indexed · h-index 19

Mark Carman

77 papers receiving 1.4k citations

Peers

Mark Carman
Comparison fields: 5 of 109
  • Artificial Intelligence 1.1k
  • Information Systems 537
  • Computer Vision and Pattern Recognition 251
  • Signal Processing 119
  • Statistical and Nonlinear Physics 88
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Citations per field
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Citations per year

Countries citing papers authored by Mark Carman

Since Specialization
Citations

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

Fields of papers citing papers by Mark Carman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Mark Carman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mark Carman Line = papers co-authored together Mark Carman links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20238
2 20236
3
Sarcasm Target Identification: Dataset and An Introductory Approach
20188
4
A little competition never hurt anyone's relevance assessments
20162
5 201617
6 201639
7
EmoGram: An Open-Source Time Sequence-Based Emotion Tracker and Its Innovative Applications
20168
8
That'll do fine! A coarse lexical resource for English-Hindi MT, using polylingual topic models
20162
9 201563
10
Using Multilingual Topic Models for Improved Alignment in English-Hindi MT
20151
11 20155
12 2013134
13
Piloted search and recommendation with social tag cloud-based navigation
20100
14
University of Lugano at TREC 2010
20104
15
University of Lugano at TREC 2009 Blog Track
20092
16 20082
17
University of Lugano at TREC 2008 Blog Track
20085
18
Learning semantic descriptions of web information sources
200710
19
Learning source descriptions for web services
20051
20
Inducing Source Descriptions for Automated Web Service Composition
20051

About Mark Carman

Mark Carman is a scholar working on Artificial Intelligence, Information Systems and Computer Science Applications, having authored 79 papers that have together received 1.6k indexed citations. Recurring topics across this work include Topic Modeling (27 papers), Natural Language Processing Techniques (14 papers), Sentiment Analysis and Opinion Mining (12 papers), Information Retrieval and Search Behavior (12 papers), Web Data Mining and Analysis (11 papers), Semantic Web and Ontologies (11 papers), Advanced Text Analysis Techniques (9 papers) and Text and Document Classification Technologies (7 papers). The work is most often cited by research in Artificial Intelligence (1.1k citations), Information Systems (537 citations) and Computer Vision and Pattern Recognition (251 citations). Mark Carman has collaborated with scholars based in Australia, Switzerland and India. Frequent co-authors include Aditya Joshi, Pushpak Bhattacharyya, Fábio Crestani, Ye Zhu, Kai Ming Ting, Morgan Harvey, Nayyar A. Zaidi, Geoffrey I. Webb, Jesús Cerquides and Vaibhav Tripathi. Their work appears in journals such as Machine Learning, Digital Investigation, Pattern Recognition, ACM Transactions on Information Systems and Language Resources and Evaluation.

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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