SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity
Impact in
Classified as
- Journal
- Joint Conference on Lexical and Computational Semantics
In The Last Decade
doi.org/w9174286 →Countries where authors are citing SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity
This map shows the geographic impact of SemEval-2012 Task 6: A Pilot on Semantic Textual 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 SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity more than expected).
Fields of papers citing SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity
This network shows the impact of SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity.
About SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity
This paper, published in 2012, received 422 indexed citations . Written by Eneko Agirre, Daniel Cer, Mona Diab and Aitor González-Agirre covering the research area of Molecular Biology and Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (408 citations), Computer Vision and Pattern Recognition (59 citations), Molecular Biology (28 citations), Information Systems (20 citations) and General Social Sciences (7 citations). Published in Joint Conference on Lexical and Computational Semantics.
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/w9174286.