Ribeka Tanaka


2020

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Towards a Versatile Medical-Annotation Guideline Feasible Without Heavy Medical Knowledge: Starting From Critical Lung Diseases
Shuntaro Yada | Ayami Joh | Ribeka Tanaka | Fei Cheng | Eiji Aramaki | Sadao Kurohashi
Proceedings of the 12th Language Resources and Evaluation Conference

Applying natural language processing (NLP) to medical and clinical texts can bring important social benefits by mining valuable information from unstructured text. A popular application for that purpose is named entity recognition (NER), but the annotation policies of existing clinical corpora have not been standardized across clinical texts of different types. This paper presents an annotation guideline aimed at covering medical documents of various types such as radiography interpretation reports and medical records. Furthermore, the annotation was designed to avoid burdensome requirements related to medical knowledge, thereby enabling corpus development without medical specialists. To achieve these design features, we specifically focus on critical lung diseases to stabilize linguistic patterns in corpora. After annotating around 1100 electronic medical records following the annotation scheme, we demonstrated its feasibility using an NER task. Results suggest that our guideline is applicable to large-scale clinical NLP projects.

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A System for Worldwide COVID-19 Information Aggregation
Akiko Aizawa | Frederic Bergeron | Junjie Chen | Fei Cheng | Katsuhiko Hayashi | Kentaro Inui | Hiroyoshi Ito | Daisuke Kawahara | Masaru Kitsuregawa | Hirokazu Kiyomaru | Masaki Kobayashi | Takashi Kodama | Sadao Kurohashi | Qianying Liu | Masaki Matsubara | Yusuke Miyao | Atsuyuki Morishima | Yugo Murawaki | Kazumasa Omura | Haiyue Song | Eiichiro Sumita | Shinji Suzuki | Ribeka Tanaka | Yu Tanaka | Masashi Toyoda | Nobuhiro Ueda | Honai Ueoka | Masao Utiyama | Ying Zhong
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education. Meanwhile, the COVID-19 condition is very different among the countries (e.g., policies and development of the epidemic), and thus citizens would be interested in news in foreign countries. We build a system for worldwide COVID-19 information aggregation containing reliable articles from 10 regions in 7 languages sorted by topics. Our reliable COVID-19 related website dataset collected through crowdsourcing ensures the quality of the articles. A neural machine translation module translates articles in other languages into Japanese and English. A BERT-based topic-classifier trained on our article-topic pair dataset helps users find their interested information efficiently by putting articles into different categories.

2016

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Building compositional semantics and higher-order inference system for a wide-coverage Japanese CCG parser
Koji Mineshima | Ribeka Tanaka | Pascual Martínez-Gómez | Yusuke Miyao | Daisuke Bekki
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing