Anna Maclachlan
Impact in
- Information Systems top 2%
- Recommender Systems and Techniques
- Linguistics and Language top 10%
Papers in
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- Natural Language Processing Techniques 5
- Machine Learning and Algorithms 2
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- Web Data Mining and Analysis 1
- Data Mining Algorithms and Applications 1
- Recommender Systems and Techniques 1
- Co-authors
- Daniel Lemire (1 shared paper)Masanori Nakamura (1 shared paper)Joel Martin (1 shared paper)Howard Johnson (1 shared paper)Owen Rambow (1 shared paper)Harold Boley (1 shared paper)Lydia White (1 shared paper)Lisa deMena Travis (1 shared paper)
- Journals
- The Canadian Journal of Linguistics / La revue canadienne de linguistique (1 paper)The Linguistic Review (1 paper)eScholarship@McGill (McGill) (1 paper)NPARC (2 papers)
In The Last Decade
Anna Maclachlan
8 papers receiving 554 citations
Anna Maclachlan's Hit Papers
Peers
Comparison fields: 5 of 54
- Information Systems 430
- Linguistics and Language 53
- Language and Linguistics 100
- Computer Vision and Pattern Recognition 153
- Transportation 48
Countries citing papers authored by Anna Maclachlan
This map shows the geographic impact of Anna Maclachlan'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 Anna Maclachlan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anna Maclachlan more than expected).
Fields of papers citing papers by Anna Maclachlan
This network shows the impact of papers produced by Anna Maclachlan. 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 Anna Maclachlan. The network helps show where Anna Maclachlan may publish in the future.
Co-authors
The 9 scholars most cited alongside Anna Maclachlan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Slope One Predictors for Online Rating-Based Collaborative Filtering Hit paper breakdown → | 2005 | 484 |
| 2 | 1997 | 65 | |
| 3 | 1995 | 30 | |
| 4 | 2003 | 23 | |
| 5 | 1992 | 8 | |
| 6 | Semantic Web Rules for Business Information. | 2005 | 5 |
| 7 | Cross-serial dependencies in Tagalog | 2002 | 5 |
| 8 | A Case Study in Implementing Dependency-Based Grammars | 1998 | 1 |
About Anna Maclachlan
Anna Maclachlan is a scholar working on Artificial Intelligence, Information Systems, Linguistics and Language, Language and Linguistics and Computer Networks and Communications, having authored 8 papers that have together received 621 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (5 papers), Machine Learning and Algorithms (2 papers), Multilingual Education and Policy (2 papers), Web Data Mining and Analysis (1 paper), Linguistic Variation and Morphology (1 paper), Advanced Database Systems and Queries (1 paper), Data Mining Algorithms and Applications (1 paper) and Recommender Systems and Techniques (1 paper). The work is most often cited by research in Information Systems (430 citations), Linguistics and Language (53 citations), Language and Linguistics (100 citations), Computer Vision and Pattern Recognition (153 citations) and Transportation (48 citations). Anna Maclachlan has collaborated with scholars based in Canada, Italy and China. Frequent co-authors include Daniel Lemire, Masanori Nakamura, Joel Martin, Howard Johnson, Owen Rambow, Harold Boley, Lydia White, Lisa deMena Travis and Marie Bourdon. Their work appears in journals such as The Canadian Journal of Linguistics / La revue canadienne de linguistique, The Linguistic Review, eScholarship@McGill (McGill) and NPARC.
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.