Papers with MedMentions
Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking (P18-1)
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| Challenge: | Existing methods to incorporate hierarchical information into knowledge bases have yielded little benefit. |
| Approach: | They propose methods to integrate hierarchical information using real bilinear mappings . they also propose two new datasets containing wide and deep hierarchies . |
| Outcome: | The proposed methods improve on flat predictions and fine-grained entity typing on FIGER dataset. |
Recognizing UMLS Semantic Types with Deep Learning (D19-62)
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Isar Nejadgholi, Kathleen C. Fraser, Berry De Bruijn, Muqun Li, Astha LaPlante, Khaldoun Zine El Abidine
| Challenge: | Entity recognition is a critical first step to a number of clinical NLP applications, such as entity linking and relation extraction. |
| Approach: | They propose to use general and domain-specific information to combine general and specific information to create a new entity recognition method. |
| Outcome: | The proposed method produces a state-of-the-art result on a newly released dataset, MedMentions. |
Leveraging Type Descriptions for Zero-shot Named Entity Recognition and Classification (2021.acl-long)
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| Challenge: | Named entity recognition and classification (NERC) tasks require annotated data for the target classes during training. |
| Approach: | They propose a novel approach that leverages the fact that textual descriptions for many entity classes occur naturally. |
| Outcome: | The proposed approach outperforms baselines adapted from machine reading comprehension and zero-shot text classification. |
Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets (D18-1)
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| Challenge: | Existing large labeled text datasets contain labels for multiple subsets of biomedical entity types, but it is rare to find large labeling datasets containing all desired entity types together. |
| Approach: | They propose a method for training a single CRF extractor from multiple datasets with disjoint or partially overlapping sets of entity types. |
| Outcome: | The proposed method improves NER F1 over training in isolation on biocreative V CDR, biocreativ VI ChemProt and MedMentions datasets. |
Entity Linking via Explicit Mention-Mention Coreference Modeling (2022.naacl-main)
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| Challenge: | Using a learning approach for entity mentions is a key component of modern entity linking systems for both candidate generation and making linking predictions. |
| Approach: | They propose a training approach that builds minimum spanning arborescences over mentions and entities to explicitly model mention coreference relationships. |
| Outcome: | The proposed approach improves candidate generation recall and link accuracy on the biomedical dataset and on MedMentions, setting a new SOTA result in linking accuracy. |
Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text (2021.findings-emnlp)
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| 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. |