NDLI: Feature extraction for the author name disambiguation problem in a bibliographic database

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Feature extraction for the author name disambiguation problem in a bibliographic database

Content Provider	ACM Digital Library
Author	Silva, Jorge M. B. Silva, Fernando
Abstract	Author name disambiguation in bibliographic databases has been, and still is, a challenging research task due to the high uncertainty there is when matching a publication author with a concrete researcher. Common approaches normally either resort to clustering to group author's publications, or use a binary classifier to decide whether a given publication is written by a specific author. Both approaches benefit from authors publishing similar works (e.g. subject areas and venues), from the previous publication history of an author (the higher, the better), and validated publication-author associations for model creation. However, whenever such an algorithm is confronted with different works from an author, or an author without publication history, often it makes wrong identifications. In this paper, we describe a feature extraction method that aims to avoid the previous problems. Instead of generally characterizing an author, it selectively uses features that associate the author to a certain publication. We build a Random Forest model to assess the quality of our set of features. Its goal is to predict whether a given author is the true author of a certain publication. We use a bibliographic database named Authenticus with more than 250,000 validated author-publication associations to test model quality. Our model achieved a top result of 95.37+ accuracy in predicting matches and 91.92+ in a real test scenario. Furthermore, in the last case the model was able to correctly predict 61.86+ of the cases where authors had no previous publication history.
Starting Page	783
Ending Page	789
Page Count	7
File Format	PDF
ISBN	9781450344869
DOI	10.1145/3019612.3019663
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-04-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Supervised learning Feature extraction Author name disambiguation Random forests Author identification
Content Type	Text
Resource Type	Article

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