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6th Workshop on Research in Computational Linguistic Typology and Multilingual NLP, SIGTYP 2024

作者： Kato, Kanji Miyagawa, So Nakagawa, Natsuko ROIS-DS Center for Open Data in Humanities National Institute for Japanese Language and Linguistics Japan

ISBN: (纸本)9798891760714

LAJaR (Language Atlas of Japanese and Ryukyuan) is a linguistic typology database focusing on micro-variation of the Japonic languages. This paper aims to report the design and progress of this ongoing database project. Finally, we also show a case study utilizing its database on zero copulas among the Japonic languages. © 2024 Association for Computational Linguistics.

关键词： database systems

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Ukiyo-e Analysis and Creativity with Attribute and Geometry Annotation 12

Ukiyo-e Analysis and Creativity with Attribute and Geometry ...

引用

12th International Conference on Computational Creativity, ICCC 2021

作者： Tian, Yingtao Clanuwat, Tarin Suzuki, Chikahiko Kitamoto, Asanobu Google Brain Tokyo Japan ROIS-DS Center for Open Data in the Humanities NII Japan

ISBN: (纸本)9789895416035

The study of Ukiyo-e, an important genre of pre-modern Japanese art, focuses on the object and style like other artwork researches. Such study has benefited from the renewed interest by the machine learning community in culturally important topics, leading to interdisciplinary works including collections of images, quantitative approaches, and machine learning-based creativities. They, however, have several drawbacks, and it remains challenging to integrate these works into a comprehensive view. To bridge this gap, we propose a holistic approach: We first present a large-scale Ukiyo-e dataset with coherent semantic labels and geometric annotations, then show its value in a quantitative study of Ukiyo-e paintings’ object using these labels and annotations. We further demonstrate the machine learning methods could help style study through soft color decomposition of Ukiyo-e, and finally provides joint insights into object and style by composing sketches and colors using colorization. We make our dataset (Tian, CODH, and ARC 2021) available online 1 © ICCC *** rights reserved.

关键词： Semantics

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Ukiyo-e analysis and creativity with attribute and geometry annotation

arXiv

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arXiv 2021年

作者： Tian, Yingtao Clanuwat, Tarin Suzuki, Chikahiko Kitamoto, Asanobu Google Brain Tokyo Japan ROIS-DS Center for Open Data in the Humanities NII

The study of Ukiyo-e, an important genre of pre-modern Japanese art, focuses on the object and style like other artwork researches. Such study has benefited from the renewed interest by the machine learning community in culturally important topics, leading to interdisciplinary works including collections of images, quantitative approaches, and machine learning-based creativities. They, however, have several drawbacks, and it remains challenging to integrate these works into a comprehensive view. To bridge this gap, we propose a holistic approach1: We first present a large-scale Ukiyo-e dataset with coherent semantic labels and geometric annotations, then show its value in a quantitative study of Ukiyo-e paintings' object using these labels and annotations. We further demonstrate the machine learning methods could help style study through soft color decomposition of Ukiyoe, and finally provides joint insights into object and style by composing sketches and colors using colorization. Copyright © 2021, The Authors. All rights reserved.

关键词： Large dataset

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KuroNet: Regularized Residual U-Nets for End-to-End Kuzushiji Character Recognition

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SN Computer Science 2020年第3期1卷 1-15页

作者： Lamb, Alex Clanuwat, Tarin Kitamoto, Asanobu Mila Université de Montréal Quebec Canada ROIS-DS Center for Open Data in the Humanities National Institute of Informatics Tokyo Japan

Kuzushiji, a cursive writing style, had been used in Japan for over a thousand years starting from the eighth century. Over 3 million books on a diverse array of topics, such as literature, science, mathematics and even cooking are preserved. However, following a change to the Japanese writing system in 1900, Kuzushiji has not been included in regular school curricula. Therefore, most Japanese natives nowadays cannot read books written or printed just 150 years ago. Museums and libraries have invested a great deal of effort into creating digital copies of these historical documents as a safeguard against fires, earthquakes and tsunamis. The result has been datasets with hundreds of millions of photographs of historical documents which can only be read by a small number of specially trained experts. Thus there has been a great deal of interest in using machine learning to automatically recognize these historical texts and transcribe them into modern Japanese characters. Our proposed model KuroNet (which builds on Clanuwat et al. in International conference on document analysis and recognition (ICDAR), 2019) outperforms other model for Kuzushiji recognition. In this paper, KuroNet achieves higher accuracy while still recognizing entire pages of text using the residual U-Net architecture from adding more regularization. We also explore areas where our system is limited and suggests directions for future work. © 2020, The Author(s).

关键词： Character recognition Historical document Japan Kuzushiji Machine learning

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Predicting the ordering of characters in Japanese historical documents

arXiv

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arXiv 2021年

作者： Lamb, Alex Clanuwat, Tarin Han, Siyu Bober-Irizar, Mikel Kitamoto, Asanobu ROIS-DS Center for Open Data in the Humanities National Institute of Informatics German Research Center for Environmental Health University of Cambridge

Japan is a unique country with a distinct cultural heritage, which is reflected in billions of historical documents that have been preserved. However, the change in Japanese writing system in 1900 made these documents inaccessible for the general public. A major research project has been to make these historical documents accessible and understandable. An increasing amount of research has focused on the character recognition task and the location of characters on image, yet less research has focused on how to predict the sequential ordering of the characters. This is because sequence in classical Japanese is very different from modern Japanese. Ordering characters into a sequence is important for making the document text easily readable and searchable. Additionally, it is a necessary step for any kind of natural language processing on the data (e.g. machine translation, language modeling, and word embeddings). We explore a few approaches to the task of predicting the sequential ordering of the characters: one using simple hand-crafted rules, another using hand-crafted rules with adaptive thresholds, and another using a deep recurrent sequence model trained with teacher forcing. We provide a quantitative and qualitative comparison of these techniques as well as their distinct trade-offs. Our best-performing system has an accuracy of 98.65% and has a perfect accuracy on 49% of the books in our dataset, suggesting that the technique is able to predict the order of the characters well enough for many tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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KaoKore: A Pre-modern Japanese Art Facial Expression dataset

arXiv

引用

arXiv 2020年

作者： Tian, Yingtao Suzuki, Chikahiko Clanuwat, Tarin Bober-Irizar, Mikel Lamb, Alex Kitamoto, Asanobu University of Cambridge MILA Université de Montréal Google Brain Tokyo Japan ROIS-DS Center for Open Data in Humanities NII

From classifying handwritten digits to generating strings of text, the datasets which have received long-time focus from the machine learning community vary greatly in their subject matter. This has motivated a renewed interest in building datasets which are socially and culturally relevant, so that algorithmic research may have a more direct and immediate impact on society. One such area is in history and the humanities, where better and relevant machine learning models can accelerate research across various fields. To this end, newly released benchmarks [3] and models [4] have been proposed for transcribing historical Japanese cursive writing, yet for the field as a whole using machine learning for historical Japanese artworks still remains largely uncharted. To bridge this gap, in this work we propose a new dataset KaoKore 1 which consists of faces extracted from pre-modern Japanese artwork. We demonstrate its value as both a dataset for image classification as well as a creative and artistic dataset, which we explore using generative models. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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