Multi-label text categorization with model combination based on F1-score maximization

Akinori Fujino, Hideki Isozaki, Jun Suzuki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

34 Citations (Scopus)

Abstract

Text categorization is a fundamental task in natural language processing, and is generally defined as a multi-label categorization problem, where each text document is assigned to one or more categories. We focus on providing good statistical classifiers with a generalization ability for multi-label categorization and present a classifier design method based on model combination and F1-score maximization. In our formulation, we first design multiple models for binary classification per category. Then, we combine these models to maximize the F1-score of a training dataset. Our experimental results confirmed that our proposed method was useful especially for datasets where there were many combinations of category labels.

Original languageEnglish
Title of host publicationIJCNLP 2008 - 3rd International Joint Conference on Natural Language Processing, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages823-828
Number of pages6
ISBN (Electronic)9780000000002
Publication statusPublished - 2008
Event3rd International Joint Conference on Natural Language Processing, IJCNLP 2008 - Hyderabad, India
Duration: 2008 Jan 72008 Jan 12

Publication series

NameIJCNLP 2008 - 3rd International Joint Conference on Natural Language Processing, Proceedings of the Conference
Volume2

Conference

Conference3rd International Joint Conference on Natural Language Processing, IJCNLP 2008
Country/TerritoryIndia
CityHyderabad
Period08/1/708/1/12

Fingerprint

Dive into the research topics of 'Multi-label text categorization with model combination based on F1-score maximization'. Together they form a unique fingerprint.

Cite this