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.
Citations per year, relative to Alan W. Biermann Alan W. Biermann (= 1×)
peers
Julie Sussman
Countries citing papers authored by Alan W. Biermann
Since
Specialization
Citations
This map shows the geographic impact of Alan W. Biermann'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 Alan W. Biermann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alan W. Biermann more than expected).
Fields of papers citing papers by Alan W. Biermann
This network shows the impact of papers produced by Alan W. Biermann. 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 Alan W. Biermann. The network helps show where Alan W. Biermann may publish in the future.
Co-authorship network of co-authors of Alan W. Biermann
This figure shows the co-authorship network connecting the top 25 collaborators of Alan W. Biermann.
A scholar is included among the top collaborators of Alan W. Biermann 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 Alan W. Biermann. Alan W. Biermann is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Hayes, Patrick J., Erik Sandewall, Saul Amarel, et al.. (2001). Logical Foundations for Cognitive Agents: Contributions in Honor of Ray Reiter. Springer eBooks.19 indexed citations
Chai, Joyce & Alan W. Biermann. (1999). The use of word sense disambiguation in an information extraction system. National Conference on Artificial Intelligence. 850–855.12 indexed citations
6.
Chai, Joyce, Alan W. Biermann, & Curry Guinn. (1999). Two dimensional generalization in information extraction. National Conference on Artificial Intelligence. 431–438.9 indexed citations
7.
Bagga, Amit & Alan W. Biermann. (1998). Coreference, cross-document coreference, and information extraction methodologies.9 indexed citations
8.
Chai, Joyce & Alan W. Biermann. (1997). Corpus Based Statistical Generalization Tree in Rule Optimization. Journal of Visual Languages & Computing.4 indexed citations
9.
Bagga, Amit, Joyce Chai, & Alan W. Biermann. (1997). The role of wordnet in the creation of a trainable message understanding system. National Conference on Artificial Intelligence. 941–948.21 indexed citations
10.
Chai, Joyce & Alan W. Biermann. (1997). The Use of Lexical Semantics in Information Extraction.8 indexed citations
11.
Bagga, Amit, et al.. (1995). A trainable system for the extraction of meaning from text. Conference of the Centre for Advanced Studies on Collaborative Research. 3.1 indexed citations
12.
Fahmy, Amr F. & Alan W. Biermann. (1993). Synthesis of real time acceptors. Journal of Symbolic Computation. 15(5-6). 807–842.3 indexed citations
Biermann, Alan W.. (1986). 24th Annual Meeting of the Association for Computational Linguistics : proceedings of the conference, 10-13 July 1986, Columbia University, New York, New York, USA.2 indexed citations
Biermann, Alan W. & Bruce W. Ballard. (1980). Toward natural language computation. Computational Linguistics. 6(2). 71–86.30 indexed citations
20.
Biermann, Alan W. & Douglas R. Smith. (1977). The Hierarchical Synthesis of Lisp Scanning Programs.. IFIP Congress. 41–45.5 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.