Bill McDowell

531 citations
8 papers · 238 · h-index 6

Impact in

    • Topic Modeling
    • Natural Language Processing Techniques
    • Advanced Text Analysis Techniques
    • Semantic Web and Ontologies
    • Speech and dialogue systems
    • Time Series Analysis and Forecasting

Papers in

Bill McDowell

8 papers receiving 226 citations

Peers

Bill McDowell
Comparison fields: 5 of 30
  • Artificial Intelligence 217
  • Signal Processing 28
  • Management Science and Operations Research 26
  • Computer Vision and Pattern Recognition 24
  • Information Systems 17
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Countries citing papers authored by Bill McDowell

Since Specialization
Citations

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

Fields of papers citing papers by Bill McDowell

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 17 scholars most cited alongside Bill McDowell, 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 Bill McDowell Line = papers co-authored together Bill McDowell links everyone, so they are left out of the graph.

All Works

8 of 8 papers shown
#Work
1 2014107
2 201494
3 202015
4
Historical Research: A Guide for Writers of Dissertations, Theses, Articles and Books
20029
5
Event Ordering with a Generalized Model for Sieve Prediction Ranking
20175
6 20195
7 20132
8
CMUML System for KBP 2015 Cold Start Slot Filling.
20151

About Bill McDowell

Bill McDowell is a scholar working on Artificial Intelligence, Computer Networks and Communications, Information Systems, Computer Vision and Pattern Recognition and Infectious Diseases, having authored 8 papers that have together received 238 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (5 papers), Topic Modeling (5 papers), Advanced Text Analysis Techniques (1 paper), Semantic Web and Ontologies (1 paper), Advanced Data Storage Technologies (1 paper), Multimodal Machine Learning Applications (1 paper), Distributed and Parallel Computing Systems (1 paper) and Service-Oriented Architecture and Web Services (1 paper). The work is most often cited by research in Artificial Intelligence (217 citations), Signal Processing (28 citations), Management Science and Operations Research (26 citations), Computer Vision and Pattern Recognition (24 citations) and Information Systems (17 citations). Bill McDowell has collaborated with scholars based in United States. Frequent co-authors include Nathanael Chambers, Taylor Cassidy, Steven Bethard, Klinton Bicknell, Burr Settles, Noah D. Goodman, Will Monroe, Stephen Mayhew, Alexander G. Ororbia and David Reitter. Their work appears in journals such as Transactions of the Association for Computational Linguistics, Theory and applications of categories and International Joint Conference on Natural Language Processing.

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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