Corpus-based and knowledge-based measures of text semantic similarity
- Journal
- University of North Texas Digital Library (University of North Texas)
In The Last Decade
doi.org/w59528320 →Countries where authors are citing Corpus-based and knowledge-based measures of text semantic similarity
This map shows the geographic impact of Corpus-based and knowledge-based measures of text semantic similarity. 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 Corpus-based and knowledge-based measures of text semantic similarity with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Corpus-based and knowledge-based measures of text semantic similarity more than expected).
Fields of papers citing Corpus-based and knowledge-based measures of text semantic similarity
This network shows the impact of Corpus-based and knowledge-based measures of text semantic similarity. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Corpus-based and knowledge-based measures of text semantic similarity.
About Corpus-based and knowledge-based measures of text semantic similarity
This paper, published in 2006, received 752 indexed citations . Written by Rada Mihalcea, Courtney D. Corley and Carlo Strapparava covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (640 citations), Information Systems (173 citations) and Molecular Biology (52 citations). Published in University of North Texas Digital Library (University of North Texas).
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.
This paper is also available at doi.org/w59528320.