Abstract
In this paper we propose a method for automatic author clustering called Document Authoring Link Retriever, DALIR. Documents are represented using Doc2Vec, experimenting with several parameters; afterwards, vectors are clustered (or linked together) using K-means and Hierarchical Agglomerative Clustering. We experimented with different vector representation sizes, different fixed number of clusters, and clustering methods. We evaluated our method on the author clustering task of PAN @ CLEF 2017. We used the BCubed F-score evaluation scheme of this task, being able to overcome some of the reported results from the first places of this challenge, although our method requires to manually establish a number of clusters a priori.
This work was done with support of the Government of Mexico via CONACYT, SNI, and Instituto Politécnico Nacional (IPN) grants SIP 2083, SIP 20200811, SIP 20201252, and SIP 20201362, IPN-COFAA and IPN-EDI.
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Calvo, H., García-Mendoza, C.V., Ruiz-Chávez, E.A., Gambino, O.J. (2020). Authorship Link Retrieval Between Documents. In: Martínez-Villaseñor, L., Herrera-Alcántara, O., Ponce, H., Castro-Espinoza, F.A. (eds) Advances in Computational Intelligence. MICAI 2020. Lecture Notes in Computer Science(), vol 12469. Springer, Cham. https://doi.org/10.1007/978-3-030-60887-3_27
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