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2018 | OriginalPaper | Buchkapitel

Hierarchical Clustering Analysis: The Best-Performing Approach at PAN 2017 Author Clustering Task

verfasst von : Helena Gómez-Adorno, Carolina Martín-del-Campo-Rodríguez, Grigori Sidorov, Yuridiana Alemán, Darnes Vilariño, David Pinto

Erschienen in: Experimental IR Meets Multilinguality, Multimodality, and Interaction

Verlag: Springer International Publishing

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Abstract

The author clustering problem consists in grouping documents written by the same author so that each group corresponds to a different author. We described our approach to the author clustering task at PAN 2017, which resulted in the best-performing system at the aforementioned task. Our method performs a hierarchical clustering analysis using document features such as typed and untyped character n-grams, word n-grams, and stylometric features. We experimented with two feature representation methods, log-entropy model, and TF-IDF, while tuning minimum frequency threshold values to reduce the feature dimensionality. We identified the optimal number of different clusters (authors) dynamically for each collection using the Caliński Harabasz score. The implementation of our system is available open source (https://github.com/helenpy/clusterPAN2017).

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Vorheriges Kapitel Deep Learning for ICD Coding: Looking for Medical Concepts in Clinical Documents in English and in French

Nächstes Kapitel Attention-Based Medical Caption Generation with Image Modality Classification and Clinical Concept Mapping

https://pan.webis.de/.

https://github.com/hhromic/python-bcubed.

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Titel: Hierarchical Clustering Analysis: The Best-Performing Approach at PAN 2017 Author Clustering Task
verfasst von: Helena Gómez-Adorno
Carolina Martín-del-Campo-Rodríguez
Grigori Sidorov
Yuridiana Alemán
Darnes Vilariño
David Pinto
Verlag: Springer International Publishing
Buch: Experimental IR Meets Multilinguality, Multimodality, and Interaction
Print ISBN: 978-3-319-98931-0

Electronic ISBN: 978-3-319-98932-7

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-98932-7_20

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