Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature (D19-57)
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| Challenge: | BB system is among the top two systems in five of all six subtasks . knowledge about microbial diversity is crucial for the study of microbiome and bacteria . |
| Approach: | They present a system that uses word embedding and lexical features to perform entities recognition, normalization and relation extraction. |
| Outcome: | The proposed system achieves state-of-the-art in five of six subtasks and is among the top two in five. |
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| Challenge: | In 2011, the Bacteria Biotope Task was conducted for the first time as a part of the BioNLP Shared Task targeting the extraction of useful information regarding bacteria and their habitats. |
| Approach: | They propose two systems for the normalization of entities and the identification of relations between entities given a biomedical text. |
| Outcome: | The proposed method performs as good as deep learning based methods which require labeled data. |
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 . |
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Bacteria Biotope Relation Extraction via Lexical Chains and Dependency Graphs (D19-57)
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| Challenge: | In this paper, we describe our approach for the Bacteria Biotopes relation extraction subtask in the BioNLP Shared Task 2019 . |
| Approach: | They propose a novel approach for dependency graph construction based on lexical chains . they then propose 'neuro network' model which uses short-term memories and syntax information . |
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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. |
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Bacteria Biotope at BioNLP Open Shared Tasks 2019 (D19-57)
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| Challenge: | The Bacteria Biotope task focuses on the extraction of the locations and phenotypes of microorganisms from PubMed abstracts and full-text excerpts. |
| Approach: | They propose to use PubMed abstracts and full-text excerpts to extract the locations and phenotypes of microorganisms and to characterizations of these entities with respect to reference knowledge sources. |
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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. |
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An ensemble CNN method for biomedical entity normalization (D19-57)
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| Challenge: | Named entity recognition (NER) and entity normalization (entity linking) are two fundamental natural language processing tasks to achieve entity normalizing. |
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BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning (2024.findings-acl)
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Qizhi Pei, Lijun Wu, Kaiyuan Gao, Xiaozhuan Liang, Yin Fang, Jinhua Zhu, Shufang Xie, Tao Qin, Rui Yan
| Challenge: | BioT5+ is an extension of the BioT5, but lacked a nuanced understanding of molecular structures. |
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Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data (P19-1)
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| Challenge: | Existing methods to perform relation extraction are feature-based or kernel-based, but the results of our study show that they can improve the performance of a baseline model with more than 10% absolute increase in F1-score. |
| Approach: | They propose a multi-task architecture which jointly trains a model to perform relation identification with cross-entropy loss and relation classification with ranking loss. |
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BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations (2023.emnlp-main)
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| Challenge: | et al., 2022) argue that the current models for drug discovery lack the ability to integrate molecules, proteins, and natural language. |
| Approach: | They propose a framework that integrates biological knowledge with chemical knowledge and natural language associations. |
| Outcome: | The proposed framework shows superior performance across a wide range of tasks. |