Andrew MacKinlay

716 citations
30 papers · 432 indexed · h-index 12
Topics
Biomedical Text Mining and Ontologies (19 papers)Topic Modeling (18 papers)Natural Language Processing Techniques (16 papers)

In The Last Decade

Andrew MacKinlay

29 papers receiving 385 citations

Peers

Andrew MacKinlay
Comparison fields: 5 of 84
  • Artificial Intelligence 313
  • Molecular Biology 116
  • Sociology and Political Science 46
  • Information Systems 37
  • Epidemiology 25
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Citations per field
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Citations per year

Countries citing papers authored by Andrew MacKinlay

Since Specialization
Citations

This map shows the geographic impact of Andrew MacKinlay'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 Andrew MacKinlay with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew MacKinlay more than expected).

Fields of papers citing papers by Andrew MacKinlay

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andrew MacKinlay. 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 Andrew MacKinlay. The network helps show where Andrew MacKinlay may publish in the future.

Co-authorship network of co-authors of Andrew MacKinlay

This figure shows the co-authorship network connecting the top 25 collaborators of Andrew MacKinlay. A scholar is included among the top collaborators of Andrew MacKinlay 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 Andrew MacKinlay. Andrew MacKinlay 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
#WorkIndexed citations
1 27
2
Temporal Modelling of Geospatial Words in Twitter.
1
3
NER for Medical Entities in Twitter using Sequence to Sequence Neural Networks.
11
4 2
5 8
6 26
7 18
8
Identifying Twitter Location Mentions
3
9
Deep Belief Networks and Biomedical Text Categorisation
12
10
Extracting Biomedical Events and Modifications Using Subgraph Matching with Noisy Training Data
9
11
How Noisy Social Media Text, How Diffrnt Social Media Sources?
124
12
Information Extraction from Medication Prescriptions Within Drug Administration Data
2
13
Simple similarity-based question answering strategies for biomedical text
4
14
The Effects of Semantic Annotations on Precision Parse Ranking
5
15 20
16 6
17
Treeblazing: Using External Treebanks to Filter Parse Forests for Parse Selection and Treebanking
2
18
Intelligent Linux Information Access by Data Mining: the ILIAD Project
8
19
A Baseline Approach to the RTE5 Search Pilot
6
20
Reconsidering language identification for written language resources
52

About Andrew MacKinlay

Andrew MacKinlay is a scholar working on Artificial Intelligence, Toxicology and Molecular Biology, having authored 30 papers that have together received 432 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (19 papers), Topic Modeling (18 papers) and Natural Language Processing Techniques (16 papers). The work is most often cited by research in Artificial Intelligence (313 citations), Health Information Management (18 citations) and Toxicology (13 citations). Andrew MacKinlay has collaborated with scholars based in Australia, United States and Norway. Frequent co-authors include Timothy Baldwin, Antonio Jimeno Yepes, Marco Lui, Paul Cook, Li Wang, David Martínez, Timothy Baldwin, Karin Verspoor, Baden Hughes and Steven Bird. Their work appears in journals such as BMC Bioinformatics, Artificial Intelligence in Medicine and BMC Medical Informatics and Decision Making.

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.

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