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.
A study on similarity and relatedness using distributional and WordNet-based approaches
2009536 citationsEneko Agirre, Enrique Alfonseca et al.profile →
This map shows the geographic impact of Marius Paşca'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 Marius Paşca with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marius Paşca more than expected).
This network shows the impact of papers produced by Marius Paşca. 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 Marius Paşca. The network helps show where Marius Paşca may publish in the future.
Co-authorship network of co-authors of Marius Paşca
This figure shows the co-authorship network connecting the top 25 collaborators of Marius Paşca.
A scholar is included among the top collaborators of Marius Paşca 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 Marius Paşca. Marius Paşca is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Gupta, Amit, Francesco Piccinno, Mikhail Kozhevnikov, Marius Paşca, & Daniele Pighin. (2016). Revisiting Taxonomy Induction over Wikipedia. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 2300–2309.10 indexed citations
2.
Paşca, Marius & Hylke Buisman. (2015). Dissecting German grammar and Swiss passports: open-domain decomposition of compositional entries in large-scale knowledge repositories. International Conference on Artificial Intelligence. 896–902.2 indexed citations
Christensen, Janara & Marius Paşca. (2012). Instance-Driven Attachment of Semantic Annotations over Conceptual Hierarchies. Conference of the European Chapter of the Association for Computational Linguistics. 503–513.1 indexed citations
5.
Reisinger, Joseph & Marius Paşca. (2011). Fine-Grained Class Label Markup of Search Queries. Meeting of the Association for Computational Linguistics. 1200–1209.8 indexed citations
6.
Paşca, Marius. (2011). Ranking Class Labels Using Query Sessions. Meeting of the Association for Computational Linguistics. 1607–1615.4 indexed citations
7.
Paşca, Marius. (2011). Attribute Extraction from Synthetic Web Search Queries. International Joint Conference on Natural Language Processing. 401–409.5 indexed citations
Agirre, Eneko, et al.. (2009). A study on similarity and relatedness using distributional and WordNet-based approaches. 19–19.536 indexed citations breakdown →
10.
Durme, Benjamin Van & Marius Paşca. (2008). Finding cars, goddesses and enzymes: parametrizable acquisition of labeled instances for open-domain information extraction. National Conference on Artificial Intelligence. 1243–1248.31 indexed citations
11.
Paşca, Marius. (2008). Turning web text and search queries into factual knowledge: hierarchical class attribute extraction. National Conference on Artificial Intelligence. 1225–1230.22 indexed citations
12.
Lin, Dekang, Shaojun Zhao, Benjamin Van Durme, & Marius Paşca. (2008). Mining Parenthetical Translations from the Web by Word Alignment. Meeting of the Association for Computational Linguistics. 994–1002.25 indexed citations
13.
Paşca, Marius & Benjamin Van Durme. (2007). What you seek is what you get: extraction of class attributes from query logs. International Joint Conference on Artificial Intelligence. 2832–2837.82 indexed citations
Harabagiu, Sanda M., et al.. (2001). Dialogue Management for Interactive Question Answering. The Florida AI Research Society. 444–448.1 indexed citations
16.
Paşca, Marius. (2001). A Relational and Logic Representation for Open-Domain Textual Question Answering.. Meeting of the Association for Computational Linguistics. 37–42.4 indexed citations
17.
Harabagiu, Sanda M., Dan Moldovan, Marius Paşca, et al.. (2000). FALCON: Boosting Knowledge for Answer Engines. University of North Texas Digital Library (University of North Texas).173 indexed citations
18.
Harabagiu, Sanda M. & Marius Paşca. (2000). Mining Textual Answers with Knowledge-Based Indicators. The Florida AI Research Society. 214–218.1 indexed citations
19.
Moldovan, Dan, Sanda M. Harabagiu, Marius Paşca, et al.. (1999). LASSO: A Tool for Surfing the Answer Net. University of North Texas Digital Library (University of North Texas).109 indexed citations
20.
Harabagiu, Sanda M. & Marius Paşca. (1999). Integrating Symbolic and Statistical Methods for Prepositional Phrase Attachment. The Florida AI Research Society. 303–307.3 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.