Extractive Medical Entity Disambiguation with Memory Mechanism and Memorized Entity Information (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods focus on local optimal while ignoring sole-mention disambiguation boosted by richer context from other mentions’ disambiguating processes. |
| Approach: | They propose an approach to extracting medical entity disambiguation using memory mechanism and memorized entity information (M3E) they use a memory mechanism module that performs memory caching, retrieval, fusion and cross-network residual to aid the disambiguations of remaining mentions. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two benchmark datasets. |
Similar Papers
Medical Entity Disambiguation with Medical Mention Relation and Fine-grained Entity Knowledge (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for medical entity disambiguation (MED) fail to fully utilize the knowledge within medical knowledge bases (KBs) Existing models overlook essential interactions between medical mentions and candidate entities, resulting in knowledge- and interaction-inefficient modeling and suboptimal disambiguations performance. |
| Approach: | They propose to combine a mention relation fusion module and an entity knowledge fusion modules to map medical mentions to corresponding entities in a knowledge base (KB) |
| Outcome: | The proposed method outperforms state-of-the-art MED models on two publicly available real-world datasets. |
LLM as Entity Disambiguator for Biomedical Entity-Linking (2025.acl-short)
Copied to clipboard
| Challenge: | Entity linking involves normalizing a mention in medical text to a unique identifier in a knowledge base, such as UMLS or MeSH. |
| Approach: | They propose to use a large language model as an entity disambiguator to enhance the accuracy of alias-matching entity linking methods. |
| Outcome: | The proposed method surpasses existing methods on biomedical datasets by up to 16 points in accuracy. |
Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for named entity disambiguation are limited by coarse-grained structural resources in biomedical knowledge bases and training datasets that provide low coverage over uncommon resources. |
| Approach: | They propose a method that integrates structural knowledge from general text knowledge bases to the medical domain. |
| Outcome: | The proposed method improves disambiguation accuracy on two benchmark medical NED datasets by up to 57 points. |
Recognizing Complex Entity Mentions: A Review and Future Directions (P18-3)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is a task of identifying and classifying named entities (NE) within text. |
| Approach: | They review existing methods for identifying and classifying named entities within text . they identify the research gap and propose a new approach to tackle these problems . |
| Outcome: | The proposed methods address the identified identified gaps in the literature and provide recommendations for future work. |
Entity Decomposition with Filtering: A Zero-Shot Clinical Named Entity Recognition Framework (2025.naacl-long)
Copied to clipboard
| Challenge: | Recent studies have demonstrated that large language models (LLMs) can perform in named entity recognition tasks. |
| Approach: | They propose a framework for clinical named entity recognition that decomposes the entity recognition task into several retrievals of sub-types and then filters them. |
| Outcome: | The proposed framework improves on the clinical named entity recognition task. |
Leveraging Dependency Forest for Neural Medical Relation Extraction (D19-1)
Copied to clipboard
| Challenge: | Existing methods for medical relation extraction use dependency syntax as a source of features. |
| Approach: | They propose a method to extract relational information from medical literature by using dependency forests. |
| Outcome: | The proposed method outperforms the standard tree-based methods in the medical domain. |
Clustering-based Inference for Biomedical Entity Linking (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing approaches to linking entities ignore relationships between entities in biomedical knowledge bases. |
| Approach: | They propose a model which can link mentions of unseen entities using learned representations of entities. |
| Outcome: | The proposed model improves on the largest publicly available biomedical dataset by 3.0 points of accuracy and 2.3 points of reliability. |
An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization (2021.acl-long)
Copied to clipboard
| Challenge: | Existing models for medical named entity recognition and named entity normalization suffer from error propagation between the two tasks. |
| Approach: | They propose an end-to-end progressive multi-task learning model for jointly modeling medical named entity recognition and normalization in an effective way. |
| Outcome: | The proposed model reduces error propagation by exploiting the learnable features for both tasks to improve performance. |
RRNorm: A Novel Framework for Chinese Disease Diagnoses Normalization via LLM-Driven Terminology Component Recognition and Reconstruction (2024.findings-acl)
Copied to clipboard
| Challenge: | Clinical Terminology Normalization (CTN) aims at finding standard terms from a given termbase for mentions extracted from clinical texts. |
| Approach: | They propose a method that leverages reasoning capability of large language models to recognize components of terms and automate decomposition. |
| Outcome: | The proposed strategy achieves state-of-the-art on the experimental dataset. |
Learning to Leverage High-Order Medical Knowledge Graph for Joint Entity and Relation Extraction (2023.findings-acl)
Copied to clipboard
| Challenge: | Medical terms are difficult to understand and relations between medical entities become complicated. |
| Approach: | They propose to leverage medical domain knowledge for extracting entities and relations for Chinese medical texts by building a heterogeneous graph based on medical knowledge graph. |
| Outcome: | The proposed method is more effective than state-of-the-art methods on real Chinese medical texts. |