Trigger Word Detection and Thematic Role Identification via BERT and Multitask Learning (D19-57)
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| Challenge: | Using natural language processing to discover and mine drug-related knowledge from text has been a hot topic in recent years. |
| Approach: | They propose to use a pre-trained biomedical language representation model to extract mutation-disease knowledge from PubMed. |
| Outcome: | The proposed approaches achieve 0.60 (ranks 1) and 0.25 (rank 2) on task 1 and task 2 respectively in terms of F1 metric. |
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Biomedical relation extraction with pre-trained language representations and minimal task-specific architecture (D19-57)
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| 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. |
DeepGeneMD: A Joint Deep Learning Model for Extracting Gene Mutation-Disease Knowledge from PubMed Literature (D19-57)
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| Challenge: | Identifying and understanding the pathogenesis of genetic diseases is an essential task. |
| Approach: | They propose a joint deep learning model for gene mutation-disease knowledge extraction that adapts the state-of-the-art hierarchical multi-task learning framework for joint inference on named entity recognition and relation extraction. |
| Outcome: | The proposed model achieves the average score of 0.45 on recognizing gene activities and disease entities and the average F1 score of 0.3 on extracting relations, ranking 1st in the AGAC RE task. |
A Multi-Task Learning Framework for Extracting Bacteria Biotope Information (D19-57)
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| Challenge: | Existing methods to extract information from unstructured text are slow or expensive to get. |
| Approach: | They propose a multi-task transfer multi-learning method for Bacteria Biotope rel+ner task . they use BERT and pre-train it using mask language models and next sentence prediction . |
| Outcome: | The proposed method achieves the best performance on all metrics including slot error rate, precision and recall in the Bacteria Biotope rel+ner subtask. |
NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) is a widely adopted NLP task . authors present three variants of NER task, with dataset to support them . |
| Approach: | They propose three variants of the NER task, together with a dataset to support them . they propose a move towards more fine-grained entities and zero-shot recognition . |
| Outcome: | The proposed model matches or surpasses existing models in NER tasks . the proposed model is based on a large, silver-annotated corpus of 4 million paragraphs . |
Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019 (D19-57)
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| Challenge: | Named Entity Recognition (NER) and Relation Extraction (RE) are essential tools in distilling knowledge from biomedical literature. |
| Approach: | They propose to use Named Entities to perform nested entities extraction, Entity Normalization and Relation Extraction to generalize the approach to different languages. |
| Outcome: | The proposed approach can be generalized to different languages and showed it’s effectiveness for English and Spanish text. |
An Overview of the Active Gene Annotation Corpus and the BioNLP OST 2019 AGAC Track Tasks (D19-57)
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| Challenge: | Biomedical natural language processing (BioNLP) has long been recognized as effective method to accelerate drug-related knowledge discovery. |
| Approach: | They developed an active gene annotation corpus (AGAC) to support drug repurposing. |
| Outcome: | The active gene annotation corpus (AGAC) was developed to support knowledge discovery for drug repurposing. |
Transfer Learning in Biomedical Named Entity Recognition: An Evaluation of BERT in the PharmaCoNER task (D19-57)
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| Challenge: | Existing methods for natural language processing are labor-intensive and skill-dependent . Currently, most biomedical natural language tasks focus on English documents . |
| Approach: | They introduce a BERT benchmark to facilitate the research of PharmaCoNER task . they evaluate two baselines based on Multilingual BERT and BioBERT on the corpus . |
| Outcome: | The proposed task is based on multilingual BERT and BioBERT on the PharmaCoNER corpus. |
Entity, Relation, and Event Extraction with Contextualized Span Representations (D19-1)
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| Challenge: | Existing frameworks for named entity recognition, relation extraction, and event extraction can be easily adapted for new tasks or datasets. |
| Approach: | They propose a framework that enumerates, refins, and scores text spans to capture local (within-sentence) and global (cross-sentent) context. |
| Outcome: | The proposed framework achieves state-of-the-art results on four datasets from a variety of domains. |
Incorporating medical knowledge in BERT for clinical relation extraction (2021.emnlp-main)
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| 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. |
A Deep Learning-Based System for PharmaCoNER (D19-57)
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Ying Xiong, Yedan Shen, Yuanhang Huang, Shuai Chen, Buzhou Tang, Xiaolong Wang, Qingcai Chen, Jun Yan, Yi Zhou
| Challenge: | Efficient access to mentions of clinical entities is very important for using clinical text. |
| Approach: | They developed a pipeline system based on deep learning methods for this shared task . it achieves a micro-average F1-score of 0.9105 on track 1 and a mini-average LSTM score of 0.8391 on track 2 . |
| Outcome: | The proposed system achieves a micro-average F1-score of 0.9105 on track 1 and a mini-average score of 0.8391 on track 2. |