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FRAG at the NTCIR-17 MedNLP-SC Task
Get PDFThe FRAG team participated in the Social Media (SM) subtask of the NTCIR-17 MedNLP-SC Task. Our approach involved fine-tuning a multilingual transformer-based model on the train set. The team ranked 3rd for English, German and Japanese based on Exact accuracy and Binary scores.conference pape
TMUNLP at the NTCIR-17 MedNLP-SC Task
Get PDFThe TMUNLP team participated in the adverse drug event (ADE) detection subtask, focusing on social media texts in English for NTCIR-17's MedNLP-SC. This paper outlines our approach to addressing the challenge. Within the ADE subtask, we primarily implemented two methods to tackle the long tail distribution issue: distribution balanced loss and data augmentation. Finally, we employed ensemble learning to enhance the performance of our model.conference pape
IMNTPU at the NTCIR-17 Real-MedNLP Task: Multi-Model Approach to Adverse Drug Event Detection from Social Media
Get PDFThe IMNTPU team engaged in the NTCIR-17 RealMedNLP task, specifically focusing on Subtask1: Adverse Drug Event detection (ADE) and the challenge of identifying related radiology reports. This task is centered on harnessing methodologies that offer significant aid in real-world medical services, especially when training resources are limited. In our approach, we harnessed the power of pre-trained language models (PLMs), particularly leveraging models like the BERT transformer, to understand both sentence and document structures. Our experimentation with diverse network designs based on PLMs paved the way for an enlightening comparative analysis. Notably, BioBERT-Base emerged as a superior contender, showcasing commendable accuracy relative to its peers. Furthermore, our investigation made strides in the realm of one-shot learning for multiclass labeling, specifically with the GPT framework. The insights gathered emphasized the necessity for more specialized strategies, suggesting avenues for future research in multiclass labeling tasks.conference pape
NTCIR-17 MedNLP-SC Social Media Adverse Drug Event Detection: Subtask Overview
Get PDFThis paper presents the Social Media Adverse Drug Event Detection (SM-ADE) subtask as part of the shared task Medical Natural Language Processing for Social Media and Clinical Texts (MedNLP- SC) at NTCIR-17. The SM-ADE subtask aims to identify a set of symptoms caused by a drug, referred to as adverse drug event (ADE) detection, within social media texts in multiple languages, including Japanese, English, French, and German. The competition attracted 26 teams, of which eight submitted official runs for the SM-ADE subtask. We believe this task will be essential to develop core technologies of practical medical applications in the near future.conference pape
CIR at the NTCIR-17 ULTRE-2 Task
Get PDFThe Chinese academy of sciences Information Retrieval team (CIR) has participated in the NTCIR-17 ULTRE-2 task. This paper describes our approaches and reports our results on the ULTRE-2 task. We recognize the issue of false negatives in the Baidu search data in this competition is very severe, much more severe than position bias. Hence, we adopt the Dual Learning Algorithm (DLA) to address the position bias and use it as an auxiliary model to study how to alleviate the false negative issue. We approach the problem from two perspectives: 1) correcting the labels for non-clicked items by a relevance judgment model trained from DLA, and learn a new ranker that is initialized from DLA; 2) including random documents as true negatives and documents that have partial matching as hard negatives. Both methods can enhance the model performance and our best method has achieved nDCG@10 of 0.5355, which is 2.66% better than the best score from the organizer.conference pape