Self-Alignment Pretraining for Biomedical Entity Representations (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing approaches to self-supervised learning of biomedical entities are limited in the biomedic domain. |
| Approach: | They propose a pretraining scheme that self-aligns the representation space of biomedical entities. |
| Outcome: | The proposed framework achieves state-of-the-art on six MEL benchmarking datasets. |
Similar Papers
Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning (2022.naacl-main)
Copied to clipboard
| Challenge: | Generative methods for biomedical entity linking (EL) use synonyms knowledge from knowledge bases (KB) this is not trivial to inject into a generative method, but it is cost-effective. |
| Approach: | They propose to inject synonyms knowledge into a generative model of biomedical EL by constructing synthetic samples with synonyms and definitions from KB and requiring the model to recover concept names. |
| Outcome: | The proposed method achieves state-of-the-art results on several biomedical EL tasks without candidate selection. |
LinkBERT: Pretraining Language Models with Document Links (2022.acl-long)
Copied to clipboard
| Challenge: | Existing language model pretraining methods do not capture dependencies or knowledge that span across documents. |
| Approach: | They propose a language model pretraining method that leverages links between documents . they use masked language modeling and document relation prediction to model LMs . |
| Outcome: | The proposed method outperforms existing methods on downstream tasks across two domains. |
Knowledge-Rich Self-Supervision for Biomedical Entity Linking (2022.findings-emnlp)
Copied to clipboard
Sheng Zhang, Hao Cheng, Shikhar Vashishth, Cliff Wong, Jinfeng Xiao, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon
| Challenge: | Entity linking is challenging in high-value domains with myriad entities . standard classification approaches suffer from the annotation bottleneck . |
| Approach: | They propose a self-supervised approach to learn domain knowledge for biomedical entity linking . it generates self-reported mention examples on unlabeled text and trains contextual encoder . |
| Outcome: | The proposed method outperforms existing methods by 20 points in accuracy on biomedical datasets. |
UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus (2021.naacl-main)
Copied to clipboard
| Challenge: | Contextual word embedding models do not take into account structured expert domain knowledge from a knowledge base. |
| Approach: | They propose a contextual embedding model that integrates domain knowledge during the pre-training process via a novel knowledge augmentation strategy. |
| Outcome: | The proposed model outperforms existing domain-specific models on common named-entity recognition (NER) and clinical natural language inference tasks. |
Guiding Large Language Models for Biomedical Entity Linking via Restrictive and Contrastive Decoding (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing attempts to apply large language models to BioEL have revealed difficulties . |
| Approach: | They propose a framework that enables large language models to adapt well to BioEL . they employ restrictive decoding to ensure the generation of valid entities . |
| Outcome: | Extensive experiments show that the framework outperforms existing LLMs. |
Biomed-Enriched: Data-Efficient Biomedical Pretraining via Paragraph-Level Annotation (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models have demonstrated remarkable capabilities across a wide range of general tasks, from question answering to code generation. |
| Approach: | They use a paragraph-level pipeline to annotate PubMed Central paragraphs . they use XLM-RoBERTa to fine-tune the pipeline and propagate annotations to the full corpus . |
| Outcome: | The proposed approach improves performance on 11 tasks while using 2.5x fewer tokens and only public data. |
A Comprehensive Evaluation of Biomedical Entity Linking Models (2023.emnlp-main)
Copied to clipboard
David Kartchner, Jennifer Deng, Shubham Lohiya, Tejasri Kopparthi, Prasanth Bathala, Daniel Domingo-Fernández, Cassie Mitchell
| Challenge: | Current methods struggle to correctly link genes and proteins and often have difficulty incorporating context into linking decisions. |
| Approach: | They evaluate nine recent state-of-the-art biomedical entity linking models under a unified framework. |
| Outcome: | The proposed models are compared along axes of accuracy, speed, ease of use, generalization, adaptability and adaptability to new ontologies and datasets. |
mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for improving multilingual models only use entity information in pretraining and do not explicitly use entities in downstream tasks. |
| Approach: | They propose to leverage Wikipedia entity representations for downstream tasks . they train a multilingual language model with 24 languages with entity representation . |
| Outcome: | The proposed model outperforms word-based models in cross-lingual transfer tasks. |
Controllable Contrastive Generation for Multilingual Biomedical Entity Linking (2023.emnlp-main)
Copied to clipboard
| Challenge: | Multilingual biomedical entity linking (MBEL) aims to map language-specific mentions in biomedically text to standardized concepts in a multilingual knowledge base (KB). |
| Approach: | They propose a prompt-based controllable contrastive generation framework for MBEL which summarizes multidimensional information of the UMLS concept mentioned in biomedical text into a natural sentence following a predefined template. |
| Outcome: | The proposed framework matches against UMLS concepts in as many languages and types as possible, thus facilitating cross-information disambiguation. |
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking (2021.acl-short)
Copied to clipboard
| Challenge: | Existing work on transferring domain-specific knowledge from a pretraining model to a resource-poor language is limited to English . a novel cross-lingual biomedical entity linking task is proposed to improve this capability. |
| Approach: | They propose a cross-lingual biomedical entity linking task and establish a new benchmark spanning 10 typologically diverse languages. |
| Outcome: | The proposed methods yield consistent gains across all target languages, sometimes up to 20 Precision@1 points, without any in-domain knowledge in the target language and without any parallel data. |