Learning co-substructures by kernel dependence maximization

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

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル26th International Joint Conference on Artificial Intelligence, IJCAI 2017
編集者Carles Sierra
出版社International Joint Conferences on Artificial Intelligence
ページ3329-3335
ページ数7
ISBN(電子版)9780999241103
DOI
出版ステータスPublished - 2017
イベント26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia
継続期間: 2017 8 192017 8 25

出版物シリーズ

名前IJCAI International Joint Conference on Artificial Intelligence
0
ISSN(印刷版)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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