# JaMIE: A Pipeline Japanese Medical Information Extraction System

arXiv: https://arxiv.org/abs/2111.04261 · HTML: https://mlsanity.com/paper/2111.04261/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 8 Nov 2021.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme](https://mlsanity.com/paper/1706.05075/) (2017)
- [Joint entity recognition and relation extraction as a multi-head selection problem](https://mlsanity.com/paper/1804.07847/) (2018)
- [Enhancing Clinical Concept Extraction with Contextual Embeddings](https://mlsanity.com/paper/1902.08691/) (2019)
- [Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction](https://mlsanity.com/paper/2009.07503/) (2020)
- [Dependency Parsing as Head Selection](https://mlsanity.com/paper/1606.01280/) (2016)

## Built on by (most cited first, 4 of 4)

- [Investigating Cost-Efficiency of LLM-Generated Training Data for Conversational Semantic Frame Analysis](https://mlsanity.com/paper/2410.06550/) (2024)
- [Building a Japanese Document-Level Relation Extraction Dataset Assisted by Cross-Lingual Transfer](https://mlsanity.com/paper/2404.16506/) (2024)
- [Causal Tree Extraction from Medical Case Reports: A Novel Task for Experts-like Text Comprehension](https://mlsanity.com/paper/2503.01302/) (2025)
- [A Joint Neural Baseline for Concept, Assertion, and Relation Extraction from Clinical Text](https://mlsanity.com/paper/2603.07487/) (2026)

## Abstract (arXiv)

We present an open-access natural language processing toolkit for Japanese medical information extraction. We first propose a novel relation annotation schema for investigating the medical and temporal relations between medical entities in Japanese medical reports. We experiment with the practical annotation scenarios by separately annotating two different types of reports. We design a pipeline system with three components for recognizing medical entities, classifying entity modalities, and extracting relations. The empirical results show accurate analyzing performance and suggest the satisfactory annotation quality, the effective annotation strategy for targeting report types, and the superiority of the latest contextual embedding models.