S2AND: A Benchmark and Evaluation System for Author Name Disambiguation
@article{Subramanian2021S2ANDAB, title={S2AND: A Benchmark and Evaluation System for Author Name Disambiguation}, author={Shivashankar Subramanian and Daniel King and Doug Downey and Sergey Feldman}, journal={2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)}, year={2021}, pages={170-179} }
Author Name Disambiguation (AND) is the task of resolving which author mentions in a bibliographic database refer to the same real-world person, and is a critical ingredient of digital library applications such as search and citation analysis. While many AND algorithms have been proposed, comparing them is difficult because they often employ distinct features and are evaluated on different datasets. In response to this challenge, we present S2AND, a unified benchmark dataset for AND on…
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References
SHOWING 1-10 OF 44 REFERENCES
On Graph-Based Name Disambiguation
- Computer ScienceJDIQ
- 2011
This article presents an effective framework named GHOST (abbreviation for GrapHical framewOrk for name diSambiguaTion), to solve the problem in digital libraries to distinguish publications written by authors with identical names, and devise a novel similarity metric.
A Web Service for Author Name Disambiguation in Scholarly Databases
- Computer Science2018 IEEE International Conference on Web Services (ICWS)
- 2018
A novel, web-based, RESTful API for searching disambiguated authors, using the PubMed database as a sample application and develops a novel algorithm that has a fast record-to-cluster match for record-based queries.
On the combination of domain-specific heuristics for author name disambiguation: the nearest cluster method
- Computer ScienceInternational Journal on Digital Libraries
- 2015
This article proposes a set of carefully designed heuristics and similarity functions, and applies supervision only to optimize such parameters for each particular dataset, and shows that this method can beat state-of-the-art supervised methods in terms of effectiveness in many situations while being orders of magnitude faster.
Data sets for author name disambiguation: an empirical analysis and a new resource
- Computer ScienceScientometrics
- 2017
A set of general requirements to future AND data sets is derived, which include both trivial requirements, like absence of errors and preservation of author order, and more substantial ones, like full disambiguation and adequate representation of publications with a small number of authors and highly variable author names.
Evaluating author name disambiguation for digital libraries: a case of DBLP
- Computer ScienceScientometrics
- 2018
DBLP’s author name disambiguation performs well even on large ambiguous name blocks but deficiently on distinguishing authors with the same names, possibly due to its hybrid disAmbiguation approach combining algorithmic disambigsuation and manual error correction.
Efficient Name Disambiguation for Large-Scale Databases
- Computer SciencePKDD
- 2006
It is proved that by recasting transitivity as density reachability in DBSCAN, transitivity is guaranteed for core points.
Hybrid Deep Pairwise Classification for Author Name Disambiguation
- Computer ScienceCIKM
- 2019
A hybrid method which takes advantage of both approaches by extracting both structure-aware features and global features and in addition, a novel way to train a global model utilizing a large number of negative samples is introduced.
Dynamic author name disambiguation for growing digital libraries
- Computer ScienceInformation Retrieval Journal
- 2015
This paper proposes a “BatchAD+IncAD” framework for dynamic author disambiguation, and proposes a novel IncAD model which aggregates metadata from a cluster of records to estimate the author’s profile such as her coauthor distributions and keyword distributions, in order to predict how likely it is that a new record is produced by the author.
Disambiguating authors in academic publications using random forests
- Computer ScienceJCDL '09
- 2009
This paper describes an algorithm for pair-wise disambiguation of author names based on a machine learning classification algorithm, random forests, and defines a set of similarity profile features to assist in author disambigsuation.
Name Disambiguation in AMiner: Clustering, Maintenance, and Human in the Loop.
- Computer ScienceKDD
- 2018
A novel representation learning method is proposed by incorporating both global and local information and an end-to-end cluster size estimation method that is significantly better than traditional BIC-based method is presented.