Lipika Dey
About
In The Last Decade
Lipika Dey
73 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 136
- Artificial Intelligence 980
- Information Systems 414
- Computer Vision and Pattern Recognition 304
- Signal Processing 197
- Management Science and Operations Research 134
Countries citing papers authored by Lipika Dey
This map shows the geographic impact of Lipika Dey'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 Lipika Dey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lipika Dey more than expected).
Fields of papers citing papers by Lipika Dey
This network shows the impact of papers produced by Lipika Dey. 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 Lipika Dey. The network helps show where Lipika Dey may publish in the future.
Co-authorship network of co-authors of Lipika Dey
This figure shows the co-authorship network connecting the top 25 collaborators of Lipika Dey. A scholar is included among the top collaborators of Lipika Dey 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 Lipika Dey. Lipika Dey is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | Ontology Guided Purposive News Retrieval and Presentation. | 2 |
| 4 | Automatic Curation and Visualization of Crime Related Information from Incrementally Crawled Multi-source News Reports | 1 |
| 5 | 41 | |
| 6 | Automatic Scoring for Innovativeness of Textual Ideas. | 14 |
| 7 | A Framework for Mining Enterprise Risk and Risk Factors from News Documents | 1 |
| 8 | 4 | |
| 9 | A framework to integrate unstructured and structured data for enterprise analytics | 7 |
| 10 | 4 | |
| 11 | Enterprise information fusion for real-time business intelligence | 10 |
| 12 | 3 | |
| 13 | 5 | |
| 14 | 21 | |
| 15 | Relation Extraction from Texts for Efficient Access to InformationApplication to Bio-Medical Text Processing. | 1 |
| 16 | 3 | |
| 17 | Information extraction and imprecise query answering from web documents | 5 |
| 18 | Using Relations to Index Biological Document Repositories for Efficient Searching. | 1 |
| 19 | 32 | |
| 20 | Using Part-of-Speech Patterns and Domain Ontology to Mine Imprecise Concepts from Text Documents. | 1 |
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.