Natural Language Processing for Disaster Management Using Conditional Random Fields

Ketmaneechairat, Hathairat; Maliyaem, Maleerat

doi:10.12720/jait.11.2.97-102

Cited by 23 publications

(2 citation statements)

References 8 publications

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“…We fine-tuned the Bio-ClincalBERT model by adding a conditional random field (CRF) layer (Ketmaneechairat and Maliyaem, 2020). This layer allows us to capture dependencies between labels.…”

Section: Methodsmentioning

confidence: 99%

Automated Extraction and Classification of Drug Prescriptions in Electronic Health Records: Introducing the PRESNER Pipeline

Colón-Ruiz,

Fitzgerald,

Segura-Bedmar

et al. 2023

Preprint

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Electronic health record (EHR) systems with prescription data offer vast potential in pharmacoepidemiology and pharmacogenomics. The large amount of clinical data recorded in these systems requires automatic processing to extract relevant information. This paper introduces PRESNER, a name entity recognition (NER) and classification pipeline for EHR prescription data.The pipeline uses the pre-trained transformer Bio-ClinicalBERT fine-tuned on UK Biobank prescription entries manually annotated with medication-related information (drug name, route of administration, pharmaceutical form, strength, and dosage) as the core NER system. Moreover, PRESNER also maps drugs to the Anatomical Therapeutic and Chemical (ATC) classification system and distinguishes between systemic and non-systemic drug products. It outperformed a baseline model combining the state-of-the-art Med7 and a dictionary-based approach from the ChEMBL database with a macro-average F1-score of 0.95 vs 0.71. In addition to UK Biobank prescription data, PRESNER can also be applied to other English prescription datasets, making it a versatile tool for researchers in the field.

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“…We fine-tuned the Bio-ClincalBERT model by adding a conditional random field (CRF) layer (Ketmaneechairat and Maliyaem, 2020). This layer allows us to capture dependencies between labels.…”

Section: Methodsmentioning

confidence: 99%

Automated Extraction and Classification of Drug Prescriptions in Electronic Health Records: Introducing the PRESNER Pipeline

Colón-Ruiz,

Fitzgerald,

Segura-Bedmar

et al. 2023

Preprint

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show abstract

“…As a subfield of natural language processing discipline, text classification has been studied in different application areas. Ketmaneechairat and Maliyaem [3] retrieved Twitter and Instagram posts related to natural disasters topics to obtain name entities from unstructured messages using different Machine Learning models. The authors compared the performances of Conditional Random Fields (CRF) and Long Short Term Memory (LSTM) and stated that CRF with optimization obtained best performance.…”

Section: Introductionmentioning

confidence: 99%

Offensive Language Detection in Social Media Using Transformers and Importance of Pre-training

2023

IJML

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Being exposed to offensive language on social media platforms is relatively higher because of anonymity and distant self-expression compared to real communication. Billions of contents are shared daily on these platforms, making it impossible to detect offensive posts with manual editorial processes. This situation arises the need for automatic detection of offensive language in social media posts to provide users' online safety. In this paper, we applied different Machine Learning (ML) models on over manually annotated 36,000 Turkish tweets to detect the use of offensive language messages automatically. According to the results, the most successful model for predicting offensive language is pretrained transformer-based ELECTRA model with 0.8216 F-1 score. We also obtained the highest F-1 score with 0.8342 in this dataset up to now by combining transformer-based ELECTRA and BERT models in an ensemble model.

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Named Entity Recognition in User-Generated Text: A Systematic Literature Review

Esmaail,

Omar,

Mohd

et al. 2024

IEEE Access

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Natural Language Processing for Disaster Management Using Conditional Random Fields

Cited by 23 publications

References 8 publications

Automated Extraction and Classification of Drug Prescriptions in Electronic Health Records: Introducing the PRESNER Pipeline

Automated Extraction and Classification of Drug Prescriptions in Electronic Health Records: Introducing the PRESNER Pipeline

Offensive Language Detection in Social Media Using Transformers and Importance of Pre-training

Named Entity Recognition in User-Generated Text: A Systematic Literature Review

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