Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Change-point detection in time-series data by relative density-ratio estimation
This map shows the geographic impact of Nigel Collier'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 Nigel Collier with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nigel Collier more than expected).
This network shows the impact of papers produced by Nigel Collier. 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 Nigel Collier. The network helps show where Nigel Collier may publish in the future.
Co-authorship network of co-authors of Nigel Collier
This figure shows the co-authorship network connecting the top 25 collaborators of Nigel Collier.
A scholar is included among the top collaborators of Nigel Collier 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 Nigel Collier. Nigel Collier is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Collier, Nigel, et al.. (2013). Exploring a Probabilistic Earley Parser for Event Composition in Biomedical Texts. Meeting of the Association for Computational Linguistics. 130–134.
10.
Lau, Jey Han, Nigel Collier, & Timothy Baldwin. (2012). On-line Trend Analysis with Topic Models: #twitter Trends Detection Topic Model Online. International Conference on Computational Linguistics. 1519–1534.112 indexed citations
11.
Takeuchi, Koichi, et al.. (2009). Bio-medical term extraction on simple rule language.
12.
Kawazoe, Ai, Lihua Jin, Mika Shigematsu, et al.. (2006). The Development of a Schema for the Annotation of Terms in the Biocaster Disease Detecting/Tracking System.. QUT ePrints (Queensland University of Technology).12 indexed citations
13.
Mullen, Tony & Nigel Collier. (2004). Sentiment Analysis using Support Vector Machines with Diverse Information Sources. Empirical Methods in Natural Language Processing. 412–418.442 indexed citations
14.
Mizuta, Yoko & Nigel Collier. (2004). An Annotation Scheme for a Rhetorical Analysis of Biology Articles. Language Resources and Evaluation.15 indexed citations
Collier, Nigel & Koichi Takeuchi. (2002). PIA-Core: Semantic annotation through example-based learning. Language Resources and Evaluation.5 indexed citations
17.
Tateisi, Yuka, Tomoko Ohta, Nigel Collier, Chikashi Nobata, & Jun’ichi Tsujii. (2000). Building an Annotated Corpus in the Molecular-Biology Domain. International Conference on Computational Linguistics. 28–34.21 indexed citations
18.
Collier, Nigel, et al.. (1999). Classification of MEDLINE Abstracts. Proceedings Genome Informatics Workshop/Genome informatics. 10(10). 290–291.2 indexed citations
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.