A Simple Data Augmentation Method to Improve the Performance of Named Entity Recognition Models in Medical Domain

Issifu, Abdul Majeed; Ganiz, Murat Can

doi:10.1109/ubmk52708.2021.9558986

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…This can be quite useful not only for classification but also for other downstream tasks. Studies show that data augmentation techniques can improve the accuracy of deep neural network models [29]. We plan to improve the dataset using other sources and data augmentation.…”

Section: Discussionmentioning

confidence: 99%

Traditional Machine Learning and Deep Learning-based Text Classification for Turkish Law Documents using Transformers and Domain Adaptation

Akca

Bayrak

Issifu

et al. 2022

2022 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)

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Natural Language Processing (NLP) is an interdisciplinary field between linguistics and computer science. Its main aim is to process natural (human) language using computer programs. Text classification is one of the main tasks of this field, and they are widely used in many different applications such as spam filtering, sentiment analysis, and document categorization. Nonetheless, there is only very little text classification work in the law domain and even less for the Turkish language. This may be attributed to the complexity within the domain. The length, complexity of documents, and use of extensive technical jargon are some of the reasons that distinguish this domain from others. Similar to the medical domain, understanding these documents requires extensive specialization. Another reason can be the scarcity of publicly available datasets. In this study, we compile sizeable unsupervised and supervised datasets from publicly available sources and experiment with several classification algorithms ranging from traditional classifiers to much more complicated deep learning and transformer-based models along with different text representations. We focus on classifying Court of Cassation decisions for their crime labels. Interestingly, the majority of the models we experiment with could be able to obtain good results. This suggests that although understanding the documents in the legal domain is complicated and requires expertise from humans, it may be relatively easier for machine learning models despite the extensive presence of the technical terms. This seems to be especially the case for transformer-based pre-trained neural language models which can be adapted to the law domain, showing high potential for future real-world applications.

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Section: Discussionmentioning

confidence: 99%

Traditional Machine Learning and Deep Learning-based Text Classification for Turkish Law Documents using Transformers and Domain Adaptation

Akca

Bayrak

Issifu

et al. 2022

2022 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)

Self Cite

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“…The division between training, validation and testing data was done randomly. To aid training, the Simpletransformers library (20) was used and all the processing was done in the Google Collab (21) environment. The parameters of all training can be seen at Table 2.…”

Section: Methodsmentioning

confidence: 99%

Challenges and Issues on Extracting Named Entities from Oncology Clinical Notes

Niero,

Souza,

Silva

et al. 2023

J Health Inform

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This article aims to describe the annotation process of a multi-institutional corpus of clinical texts in the oncology specialty and to train models for the Recognition of Named Entities. We use the annotated corpus to train models with different amounts of data and compare the model result with the amount of data used in training. The training of the models was done from the fine-tuning of the Bidirectional Encoder Representations from Transformers adapted to the medical-biological domain of the Portuguese language (BioBERTpt). To compare model behavior with increasing training data, models were trained with incremental amounts of data. As a result, we found that models trained with smaller but fully revised datasets performed better than models trained with larger datasets with little revision.

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“…Issifu et el. [16] in their work uses domain-specific language model bioBERT [15] with text data augmentation methods to increase the performance of NER Models.…”

Section: Related Workmentioning

confidence: 99%

Biomedical Named Entity Recognition Using Transformers with biLSTM + CRF and Graph Convolutional Neural Networks

Celikmasat

Akturk

Ertunc

et al. 2022

2022 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)

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One of the applications of Natural Language Processing (NLP) is to process free text data for extracting information. Information extraction has various forms like Named Entity Recognition (NER) for detecting the named entities in the free text. Biomedical named-entity extraction task is about extracting named entities like drugs, diseases, organs, etc. from texts in medical domain. In our study, we improve commonly used models in this domain, such as biLSTM+CRF model, using transformer based language models like BERT and its domainspecific variant BioBERT in the embedding layer. We conduct several experiments on several different benchmark biomedical datasets using a variety of combination of models and embeddings such as BioBERT+biLSTM+CRF, BERT+biLSTM+CRF, Fasttext+biLSTM+CRF, and Graph Convolutional Networks. Our results show a quite visible, 4% to 13%, improvements when baseline biLSTM+CRF model is initialized with pretrained language models such as BERT and especially with domain specific one like BioBERT on several datasets.

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A Simple Data Augmentation Method to Improve the Performance of Named Entity Recognition Models in Medical Domain

Cited by 4 publications

References 19 publications

Traditional Machine Learning and Deep Learning-based Text Classification for Turkish Law Documents using Transformers and Domain Adaptation

Traditional Machine Learning and Deep Learning-based Text Classification for Turkish Law Documents using Transformers and Domain Adaptation

Challenges and Issues on Extracting Named Entities from Oncology Clinical Notes

Biomedical Named Entity Recognition Using Transformers with biLSTM + CRF and Graph Convolutional Neural Networks

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