Výsledky vyhledávání - "Hidekazu Oiwa"

Autor: Xiaolan Wang, Alon Halevy, Wang-Chiew Tan, Hidekazu Oiwa, Aaron Feng, Behzad Golshan, George A. Mihaila

Publikováno v: Proceedings of the VLDB Endowment. 11:961-974

We present the Koko system that takes declarative information extraction to a new level by incorporating advances in natural language processing techniques in its extraction language. K oko is novel in that its extraction language simultaneously supp

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::15462303b0d2f94e758f78fe9f9f2d94
https://doi.org/10.14778/3213880.3213887

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Mining Words in the Minds of Second Language Learners for Learner-specific Word Difficulty

Autor: Yo Ehara, Hiroshi Nakagawa, Issei Sato, Hidekazu Oiwa

Publikováno v: Journal of Information Processing. 26:267-275

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2feaf1a0557b6456fe3fde7090f33bf7
https://doi.org/10.2197/ipsjjip.26.267

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A Comparison of Entity Matching Methods between English and Japanese Katakana

Autor: Hidekazu Oiwa, Michiharu Yamashita, Hideki Awashima

Publikováno v: Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology.

Japanese Katakana is one component of the Japanese writing system and is used to express English terms, loanwords, and onomatopoeia in Japanese characters based on the phonemes. The main purpose of this research is to find the best entity matching me

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0fb56ddd620967b76a0026f6c46ee484
https://doi.org/10.18653/v1/w18-5809

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Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach

Autor: Masashi Shimbo, Yuji Matsumoto, Takuo Hamaguchi, Hidekazu Oiwa

Publikováno v: IJCAI

Knowledge base completion (KBC) aims to predict missing information in a knowledge base.In this paper, we address the out-of-knowledge-base (OOKB) entity problem in KBC:how to answer queries concerning test entities not observed at training time. Exi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d3202286d1de42adb489e873d7c25b8
https://doi.org/10.24963/ijcai.2017/250

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The Economic Impact of Herd Behavior in the Japanese Loan Market

Autor: Ryuichi Nakagawa, Hidekazu Oiwa, Fumiko Takeda

Publikováno v: Pacific-Basin Finance Journal. 20:600-613

This paper investigates whether inefficient herd behavior of Japanese financial institutions in the domestic loan market affected the real economy during the period between 1975 and 1999. By using Japanese loan data, arranged by geographical area, we

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::aa4f50722d6fbf24fc0eb591c592e44e
https://doi.org/10.1016/j.pacfin.2012.01.002

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Knowledge Base Completion with Out-of-Knowledge-Base Entities: A Graph Neural Network Approach

Autor: Takuo Hamaguchi, Masashi Shimbo, Yuji Matsumoto, Hidekazu Oiwa

Publikováno v: Transactions of the Japanese Society for Artificial Intelligence. 33:F-H72_1

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8782d08ab43a6e6e91993959f2cfa9cb
https://doi.org/10.1527/tjsai.f-h72

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Online and Stochastic Learning with a Human Cognitive Bias

Autor: Hidekazu Oiwa, Hiroshi Nakagawa

Publikováno v: Proceedings of the AAAI Conference on Artificial Intelligence. 28

Sequential learning for classification tasks is an effective tool in the machine learning community. In sequential learning settings, algorithms sometimes make incorrect predictions on data that were correctly classified in the past. This paper expli

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::6e9480358db60839e1f406ee1e88a5a4
https://doi.org/10.1609/aaai.v28i1.8988

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Formalizing Word Sampling for Vocabulary Prediction as Graph-based Active Learning

Autor: Hidekazu Oiwa, Issei Sato, Yo Ehara, Hiroshi Nakagawa, Yusuke Miyao

Publikováno v: EMNLP

Predicting vocabulary of second language learners is essential to support their language learning; however, because of the large size of language vocabularies, we cannot collect information on the entire vocabulary. For practical measurements, we nee

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::76530f732ff39a1ddc02baf4ede0937e
https://doi.org/10.3115/v1/d14-1143

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Healing Truncation Bias: Self-Weighted Truncation Framework for Dual Averaging

Autor: Shin Matsushima, Hiroshi Nakagawa, Hidekazu Oiwa

Publikováno v: ICDM

We propose a new truncation framework for online supervised learning. Learning a compact predictive model in an online setting has recently attracted a great deal of attention. The combination of online learning with sparsity-inducing regularization

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5927536d092cac029305356d02ce804c
https://doi.org/10.1109/icdm.2012.33

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Frequency-Aware Truncated Methods for Sparse Online Learning

Autor: Hidekazu Oiwa, Shin Matsushima, Hiroshi Nakagawa

Publikováno v: Machine Learning and Knowledge Discovery in Databases ISBN: 9783642237829
ECML/PKDD (2)

Online supervised learning with L1-regularization has gained attention recently because it generally requires less computational time and a smaller space of complexity than batch-type learning methods. However, a simple L1-regularization method used

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9f71d5f6260ecd9ddb7a98b02109f3da
https://doi.org/10.1007/978-3-642-23783-6_34

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