Shuntaro Yada


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End-to-end Biomedical Entity Linking with Span-based Dictionary Matching
Shogo Ujiie | Hayate Iso | Shuntaro Yada | Shoko Wakamiya | Eiji Aramaki
Proceedings of the 20th Workshop on Biomedical Language Processing

Disease name recognition and normalization is a fundamental process in biomedical text mining. Recently, neural joint learning of both tasks has been proposed to utilize the mutual benefits. While this approach achieves high performance, disease concepts that do not appear in the training dataset cannot be accurately predicted. This study introduces a novel end-to-end approach that combines span representations with dictionary-matching features to address this problem. Our model handles unseen concepts by referring to a dictionary while maintaining the performance of neural network-based models. Experiments using two major datasaets demonstrate that our model achieved competitive results with strong baselines, especially for unseen concepts during training.


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Offensive Language Detection on Video Live Streaming Chat
Zhiwei Gao | Shuntaro Yada | Shoko Wakamiya | Eiji Aramaki
Proceedings of the 28th International Conference on Computational Linguistics

This paper presents a prototype of a chat room that detects offensive expressions in a video live streaming chat in real time. Focusing on Twitch, one of the most popular live streaming platforms, we created a dataset for the task of detecting offensive expressions. We collected 2,000 chat posts across four popular game titles with genre diversity (e.g., competitive, violent, peaceful). To make use of the similarity in offensive expressions among different social media platforms, we adopted state-of-the-art models trained on offensive expressions from Twitter for our Twitch data (i.e., transfer learning). We investigated two similarity measurements to predict the transferability, textual similarity, and game-genre similarity. Our results show that the transfer of features from social media to live streaming is effective. However, the two measurements show less correlation in the transferability prediction.

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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.