| Challenge: | Biomedical event extraction requires domain-specific knowledge and deep understanding of complex contexts. |
| Approach: | They propose a knowledge base-driven tree-structured long short-term memory networks framework . tree-LSTM framework incorporates dependency structures and entity properties from ontologies . |
| Outcome: | The proposed framework is based on the BioNLP shared task with Genia dataset and achieves state-of-the-art results. |
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| Challenge: | EHRs contain vast amounts of valuable clinical data, stored as unstructured text. |
| Approach: | They propose a method that uses existing NER+L methods to classify medical entities at scale using a named entity recognition and linking task. |
| Outcome: | The proposed model outperforms Bi-LSTM in minority class tasks with up to 28% of the time and 32% faster training time. |
Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation (2021.acl-long)
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| Challenge: | Compared with general natural language texts, sentences from scientific papers usually possess wider contexts between knowledge elements. |
| Approach: | They propose a novel biomedical Information Extraction model to extract scientific entities and events from English research papers using Abstract Meaning Representation (AMR) they construct a sentence-level knowledge graph from an external knowledge base and encode it to improve the model's understanding of complex scientific concepts. |
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Biomedical Event Extraction with Hierarchical Knowledge Graphs (2020.findings-emnlp)
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| Challenge: | Biomedical event extraction is critical in understanding biomolecular interactions described in scientific corpus. |
| Approach: | They propose to integrate domain knowledge from Unified Medical Language System (UMLS) to a pre-trained language model using Graph Edge-conditioned Attention Networks and hierarchical graph representation. |
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Text-to-Text Extraction and Verbalization of Biomedical Event Graphs (2022.coling-1)
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| Challenge: | Biomedical events represent complex, graphical, and semantically rich interactions expressed in the scientific literature. |
| Approach: | They propose a framework to solve event extraction and event verbalization with a unified text-to-text approach. |
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Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction (2021.findings-acl)
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| Challenge: | Existing models based on textual data do not capture context beyond the sentence. |
| Approach: | They propose a framework that enables the model to learn multi-omnics biological information about entities (proteins) with the help of additional multi-modal cues like molecular structure. |
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Temporal Relation Extraction in Clinical Texts: A Span-based Graph Transformer Approach (2025.acl-long)
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| Challenge: | Temporal information extraction from unstructured text is challenging due to complex clinical language, long documents, and sparse annotations. |
| Approach: | They propose a method for extracting clinical events and their temporal relations from unstructured text using the I2B2 2012 Temporal Relations Challenge corpus. |
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Dependency-Guided LSTM-CRF for Named Entity Recognition (D19-1)
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| Challenge: | Named entity recognition (NER) is one of the most important and fundamental tasks in natural language processing (NLP). |
| Approach: | They propose a dependency-guided model to encode dependency trees and capture their properties for named entity recognition. |
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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. |
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Better Feature Integration for Named Entity Recognition (2021.naacl-main)
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| Challenge: | Existing approaches to named entity recognition (NER) focus on stacking the LSTM and graph neural networks (GCNs) however, the exact interaction mechanism between the two types of features is not clear and the performance gain is not significant. |
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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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