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Found 5 papers, 1 papers with code
BLAR: Biomedical Local Acronym Resolver
NLP has emerged as an essential tool to extract knowledge from the exponentially increasing volumes of biomedical texts.
SPOT: Knowledge-Enhanced Language Representations for Information Extraction
To address these problems, we propose a new pre-trained model that learns representations of both entities and relationships from token spans and span pairs in the text respectively.
Ranked #4 on
Relation Extraction
on SemEval-2010 Task-8
Abstractified Multi-instance Learning (AMIL) for Biomedical Relation Extraction
In this work, we propose a novel reformulation of MIL for biomedical relation extraction that abstractifies biomedical entities into their corresponding semantic types.
Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data
In our paper, we compare the performance of popular model prediction metric MSE and proposed reproducibility criterion Stability in evaluating four widely used feature selection methods in both simulations and experimental microbiome applications.
Theoretical Rule-based Knowledge Graph Reasoning by Connectivity Dependency Discovery
Discovering precise and interpretable rules from knowledge graphs is regarded as an essential challenge, which can improve the performances of many downstream tasks and even provide new ways to approach some Natural Language Processing research topics.
