Biomedical Relation Classification by single and multiple source domain adaptation (D19-62)
Copied to clipboard
| Challenge: | Existing supervised systems are highly data-driven and require a lot of effort to label data for a new domain. |
| Approach: | They propose to transfer knowledge from one or more related source domains to another domain to improve relation classification. |
| Outcome: | The proposed model outperforms neural-network based models on biomedical datasets and with contextualized embeddings on 3 biomedically-relevant datasets. |
Similar Papers
Entity Enhancement for Implicit Discourse Relation Classification in the Biomedical Domain (2021.acl-short)
Copied to clipboard
| Challenge: | Discourse relation classification is a challenging task when the text domain is different from the standard Penn Discourse Treebank (PDTB) training corpus domain. |
| Approach: | They propose to use the Biomedical Discourse Relation Bank to improve discourse relational argument representation by linking explicit instances of similar relations with a voting pipeline. |
| Outcome: | The proposed model outperforms the pre-trained BioBERT model by 2% points. |
How to Encode Domain Information in Relation Classification (2024.lrec-main)
Copied to clipboard
Elisa Bassignana, Viggo Unmack Gascou, Frida Nøhr Laustsen, Gustav Kristensen, Marie Haahr Petersen, Rob van der Goot, Barbara Plank
| Challenge: | Existing deep learning models require a lot of training data to obtain high performance. |
| Approach: | They propose a multi-domain training setup for Relation Classification (RC) they compare different ways to enrich input instances with domain information . |
| Outcome: | The proposed model improves > 2 Macro-F1 against the baseline setup. |
Named Entity Recognition Under Domain Shift via Metric Learning for Life Sciences (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing models for named entity recognition fail in scientific domains such as biomedicine and chemistry. |
| Approach: | They propose a model to transfer knowledge from the biomedical domain to the target domain . they use pseudo labeling and contrastive learning to enhance discrimination . |
| Outcome: | The proposed model outperforms baseline models by up to 5% . the proposed model is based on a biomedical domain model and a chemical domain model . |
MedDistant19: Towards an Accurate Benchmark for Broad-Coverage Biomedical Relation Extraction (2022.coling-1)
Copied to clipboard
| Challenge: | Relation extraction in the biomedical domain is challenging due to the lack of labeled data and high annotation costs. |
| Approach: | They propose to use distant supervision to pair knowledge graph relationships with raw texts to tackle the scarcity of annotated data and to validate their results. |
| Outcome: | The proposed benchmarks are more accurate and consistent with existing benchmarks and show that there is no train-test leakage. |
Syntax-aware Multi-task Graph Convolutional Networks for Biomedical Relation Extraction (D19-62)
Copied to clipboard
| Challenge: | 80% of the data sets for relation extraction tasks are negative instances, resulting in a lack of syntactic information between two entity mentions. |
| Approach: | They propose a graph convolutional networks model that incorporates dependency parsing and contextualized embedding to capture comprehensive contextual information. |
| Outcome: | The proposed model achieves state-of-the-art F-score on the 2013 drug-drug interaction extraction task. |
Biomedical relation extraction with pre-trained language representations and minimal task-specific architecture (D19-57)
Copied to clipboard
| Challenge: | Using a pre-trained BERT-Base model, we learn domain-specific language representations using biomedical text. |
| Approach: | They propose a system that extends BERT, a state-of-the-art language model, which learns contextual language representations from a large unlabelled corpus. |
| Outcome: | The proposed model outperforms a baseline model while relying on an extremely simple setup with no specially engineered features. |
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. |
Bio-RFX: Refining Biomedical Extraction via Advanced Relation Classification and Structural Constraints (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for extracting structured data from unstructured texts neglect unique features of the biomedical literature, such as ambiguous entities and nested proper nouns. |
| Approach: | They propose a model that leverages sentence-level relation classification before entity extraction to tackle entity ambiguity. |
| Outcome: | The proposed model outperforms baselines in both NER and RE tasks and has competitive performance compared to the state-of-the-art fine-tuned baselines for RE. |
BioMegatron: Larger Biomedical Domain Language Model (2020.emnlp-main)
Copied to clipboard
Hoo-Chang Shin, Yang Zhang, Evelina Bakhturina, Raul Puri, Mostofa Patwary, Mohammad Shoeybi, Raghav Mani
| Challenge: | Existing studies on domain language models do not study the factors affecting performance on domain languages. |
| Approach: | They empirically evaluate factors that can affect performance on domain language applications . sub-word vocabulary set, model size, pre-training corpus, and domain transfer are important . |
| Outcome: | The results show language models trained on biomedical text perform better on biomedicine benchmarks than those trained on general domain text corpora. |
Incorporating medical knowledge in BERT for clinical relation extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) are used for diverse NLP tasks such as Information Extraction, Sentiment Analysis and Question/Answering. |
| Approach: | They propose to add medical knowledge to pre-trained language models to facilitate clinical relation extraction using a large text corpus. |
| Outcome: | The proposed model outperforms the state-of-the-art systems on the benchmark i2b2/VA 2010 clinical relation extraction dataset. |