Paramita Mirza
- Artificial Intelligence top 5%
- Management Science and Operations Research top 10%
- Information Systems top 10%
- Molecular Biology
- Computer Vision and Pattern Recognition
- Co-authors
- Sara TonelliGerhard WeikumRachele SprugnoliManuela SperanzaAndrew YatesSimon RazniewskiDenilson BarbosaAnne-Lyse Minard
- Topics
- Topic Modeling (17 papers)Natural Language Processing Techniques (12 papers)Semantic Web and Ontologies (6 papers)
- Journals
- ACM SIGIR ForumViewInstitutional Research Information System (Università degli Studi di Trento)
- Partner nations
- GermanyItalyUnited States
In The Last Decade
Paramita Mirza
21 papers receiving 315 citations
Peers
Comparison fields: 5 of 35
- Artificial Intelligence 317
- Management Science and Operations Research 59
- Information Systems 50
- Molecular Biology 18
- Computer Vision and Pattern Recognition 17
Countries citing papers authored by Paramita Mirza
This map shows the geographic impact of Paramita Mirza'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 Paramita Mirza with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paramita Mirza more than expected).
Fields of papers citing papers by Paramita Mirza
This network shows the impact of papers produced by Paramita Mirza. 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 Paramita Mirza. The network helps show where Paramita Mirza may publish in the future.
Co-authorship network of co-authors of Paramita Mirza
This figure shows the co-authorship network connecting the top 25 collaborators of Paramita Mirza. A scholar is included among the top collaborators of Paramita Mirza 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 Paramita Mirza. Paramita Mirza is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 2 | |
| 3 | 10 | |
| 4 | 25 | |
| 5 | 1 | |
| 6 | 4 | |
| 7 | 22 | |
| 8 | 6 | |
| 9 | 2 | |
| 10 | 4 | |
| 11 | 9 | |
| 12 | Expanding Wikidata's Parenthood Information by 178%, or How To Mine Relation Cardinality Information. | 2 |
| 13 | Expanding Wikidata's Parenthood Information by 178%, or How To Mine Relation Cardinalities | 3 |
| 14 | On the contribution of word embeddings to temporal relation classification | 12 |
| 15 | CATENA: CAusal and TEmporal relation extraction from NAtural language texts | 56 |
| 16 | 5 | |
| 17 | 43 | |
| 18 | An Analysis of Causality between Events and its Relation to Temporal Information | 52 |
| 19 | 50 | |
| 20 | CCG Categories for Distributional Semantic Models | 0 |
About Paramita Mirza
Paramita Mirza is a scholar working on Artificial Intelligence, Signal Processing and Information Systems and Management, having authored 22 papers that have together received 335 indexed citations. Recurring topics across this work include Topic Modeling (17 papers), Natural Language Processing Techniques (12 papers) and Semantic Web and Ontologies (6 papers). The work is most often cited by research in Artificial Intelligence (317 citations), Management Science and Operations Research (59 citations) and Information Systems (50 citations). Paramita Mirza has collaborated with scholars based in Germany, Italy and United States. Frequent co-authors include Sara Tonelli, Gerhard Weikum, Rachele Sprugnoli, Manuela Speranza, Andrew Yates, Simon Razniewski, Denilson Barbosa, Anne-Lyse Minard, Nitisha Jain and Werner Nutt. Their work appears in journals such as ACM SIGIR Forum, View and Institutional Research Information System (Università degli Studi di Trento).
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.