Masakata Kuroda


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Disease Network Constructor: a Pathway Extraction and Visualization
Mohammad Golam Sohrab | Khoa Duong | Goran Topić | Masami Ikeda | Nozomi Nagano | Yayoi Natsume-Kitatani | Masakata Kuroda | Mari Itoh | Hiroya Takamura
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

We present Disease Network Constructor (DNC), a system that extracts and visualizes a disease network, in which nodes are entities such as diseases, proteins, and genes, and edges represent regulation relation. We focused on the disease network derived through regulation events found in scientific articles on idiopathic pulmonary fibrosis (IPF). The front-end web-base user interface of DNC includes two-dimensional (2D) and 3D visualizations of the constructed disease network. The back-end system of DNC includes several natural language processing (NLP) techniques to process biomedical text including BERT-based tokenization on the basis of Bidirectional Encoder Representations from Transformers (BERT), flat and nested named entity recognition (NER), candidate generation and candidate ranking for entity linking (EL) or, relation extraction (RE), and event extraction (EE) tasks. We evaluated the end-to-end EL and end-to-end nested EE systems to determine the DNC’s back-endimplementation performance. To the best of our knowledge, this is the first attempt that addresses neural NER, EL, RE, and EE tasks in an end-to-end manner that constructs a path-way visualization from events, which we name Disease Network Constructor. The demonstration video can be accessed from We release an online system for end users and the source code is available at


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BiomedCurator: Data Curation for Biomedical Literature
Mohammad Golam Sohrab | Khoa N.A. Duong | Ikeda Masami | Goran Topić | Yayoi Natsume-Kitatani | Masakata Kuroda | Mari Nogami Itoh | Hiroya Takamura
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations

We present BiomedCurator1, a web application that extracts the structured data from scientific articles in PubMed and BiomedCurator uses state-of-the-art natural language processing techniques to fill the fields pre-selected by domain experts in the relevant biomedical area. The BiomedCurator web application includes: text generation based model for relation extraction, entity detection and recognition, text classification model for extracting several fields, information retrieval from external knowledge base to retrieve IDs, and a pattern-based extraction approach that can extract several fields using regular expressions over the PubMed and datasets. Evaluation results show that different approaches of BiomedCurator web application system are effective for automatic data curation in the biomedical domain.