Learning co-substructures by kernel dependence maximization

Sho Yokoi, Daichi Mochihashi, Ryo Takahashi, Naoaki Okazaki, Kentaro Inui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Modeling associations between items in a dataset is a problem that is frequently encountered in data and knowledge mining research. Most previous studies have simply applied a predefined fixed pattern for extracting the substructure of each item pair and then analyzed the associations between these substructures. Using such fixed patterns may not, however, capture the significant association. We, therefore, propose the novel machine learning task of extracting a strongly associated substructure pair (co-substructure) from each input item pair. We call this task dependent co-substructure extraction (DCSE), and formalize it as a dependence maximization problem. Then, we discuss critical issues with this task: the data sparsity problem and a huge search space. To address the data sparsity problem, we adopt the Hilbert-Schmidt independence criterion as an objective function. To improve search efficiency, we adopt the Metropolis-Hastings algorithm. We report the results of empirical evaluations, in which the proposed method is applied for acquiring and predicting narrative event pairs, an active task in the field of natural language processing.

Original languageEnglish
Title of host publication26th International Joint Conference on Artificial Intelligence, IJCAI 2017
EditorsCarles Sierra
PublisherInternational Joint Conferences on Artificial Intelligence
Pages3329-3335
Number of pages7
ISBN (Electronic)9780999241103
DOIs
Publication statusPublished - 2017
Event26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia
Duration: 2017 Aug 192017 Aug 25

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume0
ISSN (Print)1045-0823

Other

Other26th International Joint Conference on Artificial Intelligence, IJCAI 2017
CountryAustralia
CityMelbourne
Period17/8/1917/8/25

ASJC Scopus subject areas

  • Artificial Intelligence

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