Julie Weeds
- Artificial Intelligence top 1%
- Molecular Biology
- Information Systems top 10%
- Computer Vision and Pattern Recognition top 10%
- Language and Linguistics top 10%
- Co-authors
- David WeirDiana McCarthyJohn CarrollRob KoelingJeremy ReffinBill KellerDaoud ClarkeThomas Kober
- Topics
- Topic Modeling (22 papers)Natural Language Processing Techniques (22 papers)Speech and dialogue systems (6 papers)
- Partner nations
- United KingdomSwitzerlandItaly
In The Last Decade
Julie Weeds
31 papers receiving 848 citations
Peers
Comparison fields: 5 of 72
- Artificial Intelligence 887
- Molecular Biology 115
- Information Systems 76
- Computer Vision and Pattern Recognition 61
- Language and Linguistics 33
Countries citing papers authored by Julie Weeds
This map shows the geographic impact of Julie Weeds'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 Julie Weeds with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Julie Weeds more than expected).
Fields of papers citing papers by Julie Weeds
This network shows the impact of papers produced by Julie Weeds. 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 Julie Weeds. The network helps show where Julie Weeds may publish in the future.
Co-authorship network of co-authors of Julie Weeds
This figure shows the co-authorship network connecting the top 25 collaborators of Julie Weeds. A scholar is included among the top collaborators of Julie Weeds 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 Julie Weeds. Julie Weeds is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 12 | |
| 7 | Embed More Ignore Less (EMIL): Exploiting Enriched Representations for Arabic NLP | 1 |
| 8 | 2 | |
| 9 | 2 | |
| 10 | 7 | |
| 11 | 18 | |
| 12 | Learning to Distinguish Hypernyms and Co-Hyponyms | 82 |
| 13 | 5 | |
| 14 | 79 | |
| 15 | 15 | |
| 16 | 5 | |
| 17 | Using automatically acquired predominant senses for word sense disambiguation | 23 |
| 18 | 207 | |
| 19 | Measures and Applications of Lexical Distributional Similarity | 68 |
| 20 | 87 |
About Julie Weeds
Julie Weeds is a scholar working on Ecological Modeling, Artificial Intelligence and Linguistics and Language, having authored 35 papers that have together received 960 indexed citations. Recurring topics across this work include Topic Modeling (22 papers), Natural Language Processing Techniques (22 papers) and Speech and dialogue systems (6 papers). The work is most often cited by research in Artificial Intelligence (887 citations), Information Systems (76 citations) and Language and Linguistics (33 citations). Julie Weeds has collaborated with scholars based in United Kingdom, Switzerland and Italy. Frequent co-authors include David Weir, Diana McCarthy, John Carroll, Rob Koeling, Jeremy Reffin, Bill Keller, Daoud Clarke, Thomas Kober, David R. Weir and Lena Schmidt. Their work appears in journals such as Conservation Biology, Biological Conservation and Sleep Medicine.
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.