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Found 13 papers, 2 papers with code
The Shared Task on Gender Rewriting
In this paper, we present the results and findings of the Shared Task on Gender Rewriting, which was organized as part of the Seventh Arabic Natural Language Processing Workshop.
Sectioning of Biomedical Abstracts: A Sequence of Sequence Classification Task
In this project, we study a state-of-the-art deep learning model, which we called SSN-4 model here.
CU-UD: text-mining drug and chemical-protein interactions with ensembles of BERT-based models
Our system obtained 0. 7708 in precision and 0. 7770 in recall, for an F1 score of 0. 7739, demonstrating the effectiveness of using ensembles of BERT-based language models for automatically detecting relations between chemicals and proteins.
Improving BERT Model Using Contrastive Learning for Biomedical Relation Extraction
In this work, we explore the method of employing contrastive learning to improve the text representation from the BERT model for relation extraction.
Investigation of BERT Model on Biomedical Relation Extraction Based on Revised Fine-tuning Mechanism
In this paper, we will investigate the method of utilizing the entire layer in the fine-tuning process of BERT model.
Adversarial Learning for Supervised and Semi-supervised Relation Extraction in Biomedical Literature
Adversarial training is a technique of improving model performance by involving adversarial examples in the training process.
Identifying Comparative Structures in Biomedical Text
Comparison sentences are very commonly used by authors in biomedical literature to report results of experiments.
Noise Reduction Methods for Distantly Supervised Biomedical Relation Extraction
Distant supervision has been applied to automatically generate labeled data for biomedical relation extraction.
Rapid Adaptation of POS Tagging for Domain Specific Uses
Part-of-speech (POS) tagging is a fundamental component for performing natural language tasks such as parsing, information extraction, and question answering.
Taking into Account the Differences between Actively and Passively Acquired Data: The Case of Active Learning with Support Vector Machines for Imbalanced Datasets
Actively sampled data can have very different characteristics than passively sampled data.
A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping
A survey of existing methods for stopping active learning (AL) reveals the needs for methods that are: more widely applicable; more aggressive in saving annotations; and more stable across changing datasets.
An Approach to Reducing Annotation Costs for BioNLP
There is a broad range of BioNLP tasks for which active learning (AL) can significantly reduce annotation costs and a specific AL algorithm we have developed is particularly effective in reducing annotation costs for these tasks.
