Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Improving Distributional Similarity with Lessons Learned from Word Embeddings
2015700 citationsYoav Goldberg, Ido Dagan et al.profile →
context2vec: Learning Generic Context Embedding with Bidirectional LSTM
2016296 citationsJacob Goldberger, Ido Dagan et al.profile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
citations ·
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This map shows the geographic impact of Ido Dagan'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 Ido Dagan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ido Dagan more than expected).
This network shows the impact of papers produced by Ido Dagan. 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 Ido Dagan. The network helps show where Ido Dagan may publish in the future.
Co-authorship network of co-authors of Ido Dagan
This figure shows the co-authorship network connecting the top 25 collaborators of Ido Dagan.
A scholar is included among the top collaborators of Ido Dagan 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 Ido Dagan. Ido Dagan is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Mamou, Jonathan, et al.. (2018). SetExpander: End-to-end Term Set Expansion Based on Multi-Context Term Embeddings. International Conference on Computational Linguistics. 58–62.1 indexed citations
10.
Shnarch, Eyal, Erel Segal-Halevi, Jacob Goldberger, & Ido Dagan. (2013). PLIS: a Probabilistic Lexical Inference System. Meeting of the Association for Computational Linguistics. 97–102.1 indexed citations
11.
Kotlerman, Lili, et al.. (2012). Sentence Clustering via Projection over Term Clusters. Joint Conference on Lexical and Computational Semantics. 1. 38–43.3 indexed citations
12.
Dagan, Ido, et al.. (2012). BIUTEE: A Modular Open-Source System for Recognizing Textual Entailment. Meeting of the Association for Computational Linguistics. 73–78.14 indexed citations
13.
Shnarch, Eyal, Jacob Goldberger, & Ido Dagan. (2011). A Probabilistic Modeling Framework for Lexical Entailment. Meeting of the Association for Computational Linguistics. 558–563.11 indexed citations
14.
Mirkin, Shachar, Roy Bar-Haim, Ido Dagan, et al.. (2009). Addressing Discourse and Document Structure in the RTE Search Task.. Theory and applications of categories.11 indexed citations
15.
Szpektor, Idan, Ido Dagan, Roy Bar-Haim, & Jacob Goldberger. (2008). Contextual Preferences. Meeting of the Association for Computational Linguistics. 683–691.31 indexed citations
16.
Szpektor, Idan, Eyal Shnarch, & Ido Dagan. (2007). Instance-based Evaluation of Entailment Rule Acquisition. Meeting of the Association for Computational Linguistics. 456–463.49 indexed citations
17.
Glickman, Oren, Ido Dagan, & Moshe Koppel. (2005). Web Based Probabilistic Textual Entailment. Current Opinion in Genetics & Development. 4(5). 696–702.41 indexed citations
18.
Szpektor, Idan, Hristo Tanev, Ido Dagan, & Bonaventura Coppola. (2004). Scaling Web-based Acquisition of Entailment Relations.. Institutional Research Information System (Università degli Studi di Trento). 41–48.118 indexed citations
Dagan, Ido & Alon Itai. (1994). Word sense disambiguation using a second language monolingual corpus. Computational Linguistics. 20(4). 563–596.174 indexed citations
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.