Countries citing papers authored by Dan Goldwasser
Since
Specialization
Citations
This map shows the geographic impact of Dan Goldwasser'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 Dan Goldwasser with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Goldwasser more than expected).
This network shows the impact of papers produced by Dan Goldwasser. 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 Dan Goldwasser. The network helps show where Dan Goldwasser may publish in the future.
Co-authorship network of co-authors of Dan Goldwasser
This figure shows the co-authorship network connecting the top 25 collaborators of Dan Goldwasser.
A scholar is included among the top collaborators of Dan Goldwasser 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 Dan Goldwasser. Dan Goldwasser is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Nanda, Gaurav, et al.. (2018). Understanding Learners' Opinion about Participation Certificates in Online Courses Using Topic Modeling.. Educational Data Mining.5 indexed citations
Goldwasser, Dan, et al.. (2018). Structured Representation Learning for Online Debate Stance Prediction. International Conference on Computational Linguistics. 3728–3739.12 indexed citations
10.
Johnson, Kristen & Dan Goldwasser. (2016). “All I know about politics is what I read in Twitter”: Weakly Supervised Models for Extracting Politicians’ Stances From Twitter. International Conference on Computational Linguistics. 2966–2977.18 indexed citations
11.
Wood, Paul, et al.. (2015). SNIPE: signature generation for phishing emails. 14.1 indexed citations
Goldwasser, Dan & Dan Roth. (2013). Leveraging Domain-Independent Information in Semantic Parsing. Meeting of the Association for Computational Linguistics. 2. 462–466.4 indexed citations
14.
Goldwasser, Dan, Vivek Srikumar, & Dan Roth. (2012). Predicting Structures in NLP: Constrained Conditional Models and Integer Linear Programming in NLP. North American Chapter of the Association for Computational Linguistics. 8.3 indexed citations
15.
Goldwasser, Dan, Roi Reichart, James Clarke, & Dan Roth. (2011). Confidence Driven Unsupervised Semantic Parsing. Meeting of the Association for Computational Linguistics. 1486–1495.49 indexed citations
16.
Clarke, James, Dan Goldwasser, Ming‐Wei Chang, & Dan Roth. (2010). Driving Semantic Parsing from the World's Response. 18–27.138 indexed citations
17.
Chang, Ming‐Wei, Vivek Srikumar, Dan Goldwasser, & Dan Roth. (2010). Structured Output Learning with Indirect Supervision. International Conference on Machine Learning. 199–206.35 indexed citations
18.
Chang, Ming‐Wei, Dan Goldwasser, Dan Roth, & Vivek Srikumar. (2010). Discriminative Learning over Constrained Latent Representations. North American Chapter of the Association for Computational Linguistics. 429–437.44 indexed citations
19.
Sammons, Mark, V. G. Vinod Vydiswaran, Tim Vieira, et al.. (2009). Relation Alignment for Textual Entailment Recognition.. Theory and applications of categories.21 indexed citations
20.
Berkovsky, Shlomo, Dan Goldwasser, Tsvi Kuflik, & Francesco Ricci⋆. (2006). Identifying Inter-Domain Similarities through Content-Based Analysis of Hierarchical Web-Directories. View. 789–790.3 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.