Papers by Makoto Miwa
BENNERD: A Neural Named Entity Linking System for COVID-19 (2020.emnlp-demos)
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| Challenge: | a biomedical entity linking system is available for COVID-19 research. |
| Approach: | They propose a biomedical entity linking system that detects named enti- ties in text and links them to the UMLS knowledge base. |
| Outcome: | The proposed system detects named enti- ties in text and links them to the unified medical language system (UMS) knowledge base entries. |
Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature (2020.lrec-1)
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| Challenge: | synthesis process is essential for computational experiment design in the field of inorganic materials chemistry. |
| Approach: | They propose a corpus of the synthesis process for all-solid-state batteries and an automated machine reading system for extracting the buried synthesis processes. |
| Outcome: | The proposed system can detect entities with a macro-averaged F1 score of 0.826 while the rule-based relation extractor can achieve high performance. |
A Walk-based Model on Entity Graphs for Relation Extraction (P18-2)
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| Challenge: | Existing models treat each relation in a sentence individually, but a graph-based model needs to consider multiple relations between entities to model the dependencies among them. |
| Approach: | They propose a graph-based neural network model that treats multiple pairs in a sentence simultaneously and considers interactions among them. |
| Outcome: | The proposed model performs comparable to the state-of-the-art systems on the ACE 2005 dataset without external tools. |
Deep Exhaustive Model for Nested Named Entity Recognition (D18-1)
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| Challenge: | Named entity recognition (NER) is a task of finding entities with specific semantic types such as Protein, Cell, and RNA in text. |
| Approach: | They propose a deep neural model for nested named entity recognition . they enumerate all possible regions or spans as potential entity mentions . |
| Outcome: | The proposed model outperforms state-of-the-art models on nested and flat NER . it achieves 77.1% and 78.4% respectively in terms of F-score, without external knowledge resources. |
Span-based Named Entity Recognition by Generating and Compressing Information (2023.eacl-main)
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| Challenge: | Existing work on Named Entity Recognition (NER) only used generative or information compression models to improve performance. |
| Approach: | They propose to combine two types of IB models into one system to enhance Named Entity Recognition (NER) they incorporate unsupervised generative components span reconstruction and synonym generation into a span-based NER system. |
| Outcome: | The proposed model focuses on learning span representation, which is applicable not only to span-based NER but also to other span-related tasks such as event coreference resolution and question answering. |
Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network (P19-1)
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| Challenge: | Existing methods for inter-sentence relation extraction do not fully exploit such dependencies. |
| Approach: | They propose a model that captures local and non-local dependencies using multi-instance learning and bi-affine pairwise scoring to predict the relation of an entity pair. |
| Outcome: | The proposed model performs comparable to state-of-the-art models on biochemistry datasets. |
Principled Self-Correction in Discrete Diffusion: A UCB-Guided Framework for Text Generation (2026.eacl-long)
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| Challenge: | Existing diffusion models are trained on corrupted ground-truth tokens, but at inference time they must denoise inputs corruptes from their own predictions. |
| Approach: | They propose a framework that denoises inputs corrupted from their own predictions at inference time. |
| Outcome: | The proposed framework achieves higher faithfulness and coherence over existing diffusion baselines. |
Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs (D19-1)
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| Challenge: | Existing approaches to document-level relation extraction use nodes and edges as relations between nodes. |
| Approach: | They propose an edge-oriented graph neural model for document-level relation extraction that uses different types of nodes and edges to create a document-based graph. |
| Outcome: | The proposed model can learn intra- and inter-sentence relations using multi-instance learning internally. |
Improving Relation Extraction by Sequence-to-sequence-based Dependency Parsing Pre-training (2025.coling-main)
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| Challenge: | Existing studies show that dependency information is used only for encoder-only-based relation extraction tasks. |
| Approach: | They propose a syntax-aware seq2seq pre-trained model for relation extraction that incorporates dependency information into a seq2-trained language model by continual pre-training with a dependency parsing task. |
| Outcome: | The proposed model incorporates dependency information into a seq2seq pre-trained language model by continual pre-training with a generative sequence-to-sequence (sequ2sq)-based dependency parsing task. |
A Neural Layered Model for Nested Named Entity Recognition (N18-1)
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| Challenge: | Entity mentions embedded in longer entity mentions are referred to as nested entities due to the properties of natural language. |
| Approach: | They propose a neural model that dynamically stacks flat NER layers to identify nested entities by capturing sequential context representation with bidirectional long-term memory. |
| Outcome: | The proposed model outperforms state-of-the-art feature-based systems on nested NER, achieving 74.7% and 72.2% on GENIA and ACE2005 datasets, respectively in terms of F-score. |
Enhancing NER by Harnessing Multiple Datasets with Conditional Variational Autoencoders (2025.acl-short)
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| Challenge: | Named Entity Recognition (NER) is a fundamental NLP task . supervised learning or full fine-tuning remains essential for high performance NER models. |
| Approach: | They propose to integrate CVAE into a span-based Named Entity Recognition model. |
| Outcome: | The proposed method achieves better performance on the BioRED dataset. |
Ontology-Style Relation Annotation: A Case Study (2020.lrec-1)
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| Challenge: | Existing methods for Relation Extraction (RE) annotations use links between entities . a domain link connects the relation mention to the source entity while a range link connect the relation to the destination entity. |
| Approach: | They propose an Ontology-Style Relation (OSR) annotation approach to find relation mentions in relation annotations. |
| Outcome: | The proposed approach can be easily converted to Ontology RDF triples to populate an Ontologies. |
Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors (2021.naacl-main)
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| Challenge: | Existing methods to facilitate distantly supervised relation extraction are noisy instances, long-tail relations and unbalanced bag sizes. |
| Approach: | They propose a multi-task approach to facilitate distantly supervised relation extraction by bringing closer the representations of sentences that contain the same Knowledge Base pairs. |
| Outcome: | The proposed approach improves performance on two datasets created via distant supervision. |
Reviving Iterative Refinement in Diffusion-based NER with an Initializer-Restorer Approach (2026.acl-short)
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| Challenge: | Named Entity Recognition (NER) is a fundamental task in Information Extraction. |
| Approach: | They propose a generative paradigm for Named Entity Recognition by modeling NER as a boundary diffusion process. |
| Outcome: | The proposed model performs better than baseline on ACE2004, GENIA, and CleanCoNLL. |
Mitigating Degree Bias in Hypergraphs via Attribute-as-Structure Approach (2026.eacl-long)
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| Challenge: | Existing “attribute-as-feature” approaches do not create new aggregation pathways for sparsely connected entities. |
| Approach: | They propose an “attribute-as-structure” approach specifically designed for heterogeneous hypergraphs that integrates attributes directly into the hypergraph topology as distinct node types. |
| Outcome: | The proposed approach integrates attributes directly into the hypergraph topology as distinct node types, creating new structural pathways to enrich sparsely connected entities while preserving semantic distinctiveness within complex complex interactions. |
Addressing the Training-Inference Discrepancy in Discrete Diffusion for Text Generation (2025.coling-main)
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| Challenge: | Existing discrete diffusion models for text generation have a discrepancy between training and inference. |
| Approach: | They propose a training schema that considers two-step diffusion processes and a scheduling technique that gradually increases the probability of using self-generated text as training progresses. |
| Outcome: | The proposed training schema and scheduling technique improve diffusion models on four widely used datasets. |
A Neural Edge-Editing Approach for Document-Level Relation Graph Extraction (2021.findings-acl)
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| Challenge: | Existing methods to extract relation information from documents do not capture interactions between entities. |
| Approach: | They propose an iterative approach to extract relation information from a document . they initialize relation graphs using a pretrained transformer model and a graph convolutional neural network model . |
| Outcome: | The proposed method extracts relation information from a document using a rule-based system and empty graphs. |
A Search-based Neural Model for Biomedical Nested and Overlapping Event Detection (D19-1)
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| Challenge: | Existing structured prediction tasks target nested and overlapping events . a new structured prediction model is proposed that uses a relation graph to detect overlapping and nesting events. |
| Approach: | They propose a search-based neural network structured prediction model that treats the task as a searching problem on a relation graph of trigger-argument structures. |
| Outcome: | The proposed model performs comparable to the state-of-the-art model Turku Event Extraction System (TEES) on the BioNLP Cancer Genetics (CG) Shared Task 2013 without the use of syntactic and hand-engineered features. |
ELAINE-medLLM: Lightweight English Japanese Chinese Trilingual Large Language Model for Bio-medical Domain (2025.coling-main)
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Ken Yano, Zheheng Luo, Jimin Huang, Qianqian Xie, Masaki Asada, Chenhan Yuan, Kailai Yang, Makoto Miwa, Sophia Ananiadou, Jun’ichi Tsujii
| Challenge: | Existing bilingual or multilingual medical LLMs are limited in multilingual data and therefore perform poorly in non-English languages such as Japanese and Chinese. |
| Approach: | They propose to use a trilingual (English, Japanese, Chinese) large language model adapted for the bio-medical domain to harness the knowledge and abilities of the base model. |
| Outcome: | The proposed model can support English, Japanese, and Chinese and is adapted for a bio-medical domain. |
Learning Disentangled Representations of Negation and Uncertainty (2022.acl-long)
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| Challenge: | Negation and uncertainty modeling are long-standing tasks in natural language processing. |
| Approach: | They propose to disentangle negation, uncertainty, and content using a Variational Autoencoder by supervising latent representations using auxiliary objectives. |
| Outcome: | The proposed model can disentangle negation, uncertainty, and content using a Variational Autoencoder. |
Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information (P18-2)
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| Challenge: | Graph Convolutional Networks (GCNs) can extract drug-drug interactions (DDIs) from texts using external drug molecular structure information. |
| Approach: | They propose a novel neural method to extract drug-drug interactions (DDIs) from texts using external drug molecular structure information. |
| Outcome: | The proposed model can extract drug-drug interactions (DDIs) from texts with high accuracy and the molecular information can enhance text-based extraction by 2.39 percent points in the F-score on the DDIExtraction 2013 shared task data set. |
Coreference Resolution in Full Text Articles with BERT and Syntax-based Mention Filtering (D19-57)
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| Challenge: | Existing systems for coreference resolution are difficult because of their long coreferent chains. |
| Approach: | They propose to use an existing span-based neural coreference resolution system as a baseline . they filter noisy mentions based on parse trees and integrate a highly expressive language model into the system . |
| Outcome: | The proposed system outperforms the baseline system on the CRAFT Shared Tasks 2019 task. |
A Neural Pipeline Approach for the PharmaCoNER Shared Task using Contextual Exhaustive Models (D19-57)
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| Challenge: | NER and concept indexing perform named entity recognition and concept identifiers (CUIs) in a knowledge base. |
| Approach: | They propose a neural pipeline approach that performs named entity recognition (NER) and concept indexing (CI) they use bi-LSTM to capture the semantic information of a sequence and classify them into entities or no entities . |
| Outcome: | The proposed approach performs named entity recognition (NER) and concept indexing (CI) which links them to concept unique identifiers (CUIs) in a knowledge base. |