Sarah Zelikovitz

20 papers receiving 241 citations

Peers

Sarah Zelikovitz
Comparison fields: 5 of 44
  • Artificial Intelligence 225
  • Information Systems 86
  • Computer Vision and Pattern Recognition 45
  • Signal Processing 17
  • Computer Networks and Communications 13
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Egidio L. Terra Canada
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Citations per year

Countries citing papers authored by Sarah Zelikovitz

Since Specialization
Citations

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

Fields of papers citing papers by Sarah Zelikovitz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sarah Zelikovitz

This figure shows the co-authorship network connecting the top 25 collaborators of Sarah Zelikovitz. A scholar is included among the top collaborators of Sarah Zelikovitz 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 Sarah Zelikovitz. Sarah Zelikovitz 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
#WorkIndexed citations
1
Designing computer-based assessments: multidisciplinary findings and student perspectives
3
2
Using Decision Trees to Find Patterns in an Ophthalmology Dataset
5
3
Integrating research projects in CS1
2
4 0
5
Robotics As A Component Of A General Education Course
1
6
Student Feedback on Robotics in CS1.
1
7
Improving Text Classification with LSI Using Background Knowledge
2
8 11
9
Using Web Searches on Important Words to Create Background Sets for LSI Classification.
12
10 7
11
Evaluation of Background Knowledge for Latent Semantic Indexing Classification
1
12
Improving Text Classification Using EM with Background Text.
3
13 20
14
Transductive LSI for Short Text Classification Problems.
27
15
Mining for Features to Improve Classification.
3
16
Integrating background knowledge into text classification
2
17
Using background knowledge to improve text classification
3
18 107
19 13
20
Improving Short-Text Classification using Unlabeled Data for Classification Problems
39

About Sarah Zelikovitz

Sarah Zelikovitz is a scholar working on Architecture, Computer Science Applications and Artificial Intelligence, having authored 21 papers that have together received 272 indexed citations. Recurring topics across this work include Text and Document Classification Technologies (14 papers), Topic Modeling (7 papers) and Natural Language Processing Techniques (5 papers). The work is most often cited by research in Artificial Intelligence (225 citations), Information Systems (86 citations) and Computer Vision and Pattern Recognition (45 citations). Sarah Zelikovitz has collaborated with scholars based in United States and Australia. Frequent co-authors include Haym Hirsh, Isak Taksa, Amanda Spink, William W. Cohen, Irene H. Ludwig and Ryan J. Kettler. Their work appears in journals such as Information Retrieval, International Journal of Pattern Recognition and Artificial Intelligence and International Journal of Web Information Systems.

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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