Named entity recognition in Turkish: A comparative study with detailed error analysis

Ozcelik, Oguzhan; Toraman, Çağrı

doi:10.1016/j.ipm.2022.103065

Cited by 9 publications

(2 citation statements)

References 26 publications

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“…NLP algorithms and models are designed to handle the complexities of language [1], [4], [6], [7], [8], [9]. NER stands for Name Entity Recognition, is a task of NLP that involves identifying and classifying named entities, like names of person, location, organization and data [3], [5], [10], [11], [12], [13], [14], [15]. It helps machines to extract meaningful information from unstructured text data.…”

Section: Introductionmentioning

confidence: 99%

State-of-art approach for Indian Language based on NER: Comprehensive Review

Pandey,

Nathani

2024

Preprint

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Named Entity Recognition (NER) is a fundamental task of natural language processing (NLP) that focuses on the identification and classification of named entities such as name of individual persons, location, organization and dates within the text. NER plays a pivotal role in various NLP applications, including information extraction, question answering, text summarization and sentiment analysis. Natural language processing's (NLPs) fundamental issue is named entity recognition (NER). While extensive research has been conducted on NER for English and Hindi, the complexities of Indian languages present unique challenges that require customized solutions. Working with NER for Indian languages is a difficult endeavor with limited resources available. This article provides a comprehensive review of NER approaches tailored for Indian languages. Indian languages pose unique challenges to NER due to their rich morphological and syntactic variations, script diversity and limited annotated data availability. This paper reviews the various techniques and methodologies employed in NER for Indian languages, including rule-based, machine learning and deep learning approaches. It analyzes the strengths and limitations of each approach. Additionally, this article examines the recent advancements in transfer learning and multilingual models, showcasing their potential in improving NER performance across Indian languages. This paper aims to guide researchers and practitioners in the development of NER systems for Indian languages and foster further advancements in this field. This article also provides a comprehensive review of the diverse approaches employed for NER in Indian languages, highlighting the strength and limitations as well.

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Section: Introductionmentioning

confidence: 99%

State-of-art approach for Indian Language based on NER: Comprehensive Review

Pandey,

Nathani

2024

Preprint

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“…As Ozcelik's team and Akhtyamova explored, error analysis is critical for understanding performance nuances, especially in identifying and categorizing short-and long-term entities. This comprehensive evaluation approach underscores the complexities and challenges in developing accurate and efficient NER models for medical informatics [24,25].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study (Preprint)

Liu,

Wang,

Xiu

et al. 2024

Preprint

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BACKGROUND Named Entity Recognition (NER) models play a pivotal role in deciphering unstructured medical texts by identifying diseases, treatments, and conditions, thereby advancing clinical decision-making and research. Machine learning innovations, especially in deep learning, have notably enhanced NER capabilities. Yet, their performance is inconsistent across medical datasets due to the complexity of medical terminology and linguistic variety. Prior studies have predominantly analyzed general NER performance, overlooking specific applications in medical scenarios and the challenges therein. Moreover, an in-depth analysis of how leading models and macro-factors, such as linguistic composition, affect NER accuracy is needed. This deficiency impedes the refinement of NER models for medical applications, which is vital for improving patient outcomes and the efficiency of healthcare services. OBJECTIVE This study aims to meticulously evaluate the performance of BioBERT, RoBERTa, BigBird, and DeBERTa NER models within medical text analysis, concentrating on varied medical datasets to determine how complex medical terminology and linguistic diversity affect entity recognition accuracy. It also examines the role of macro-factors, including the lexical composition of entity phrases, in influencing the efficacy of specific models. The goal is to bridge the current research gap by offering insights that facilitate refining NER models for medical use, ultimately advancing patient care and healthcare service efficiency. METHODS This study conducts a thorough evaluation of four prominent NER models: BioBERT, RoBERTa, BigBird, and DeBERTa. The focus is assessing prediction accuracy, training efficiency, computational resource use (CPU and GPU), etc. We utilized three diverse medical datasets-Revised JNLPBA, BC5CDR, and AnatEM-selected for their relevance to the medical field. Furthermore, the study explores the impact of significant macro-factors, like the number of words in an entity phrase, on the models’ performance. A systematic analysis of these factors’ influence on prediction accuracy across the datasets was performed, aiming to gain an in-depth understanding of the impact of different macro-factors on the prediction accuracy of the medical NER model. RESULTS The analysis shows that the BioBERT model exceeded the performance of other models in prediction accuracy across the Revised JNLPBA, BC5CDR, and AnatEM medical datasets, highlighting its superior proficiency in identifying medical entities. Nevertheless, its accuracy was not consistently superior across all entity types. Additionally, the research confirmed that macro-factors, such as the number of words in an entity phrase, markedly affect the prediction accuracy of the models. CONCLUSIONS This study highlights the essential role of NER models in medical informatics, emphasizing the imperative for model optimization via precise data targeting and fine-tuning. The insights from this study will notably improve clinical decision-making and facilitate the creation of more sophisticated and effective medical NER models.

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Application of BiLSTM-CRF model with different embeddings for product name extraction in unstructured Turkish text

Arslan

2024

Neural Comput & Applic

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Named entity recognition (NER) plays a pivotal role in Natural Language Processing by identifying and classifying entities within textual data. While NER methodologies have seen significant advancements, driven by pretrained word embeddings and deep neural networks, the majority of these studies have focused on text with well-defined grammar and structure. A significant research gap exists concerning NER in informal or unstructured text, where traditional grammar rules and sentence structure are absent. This research addresses this crucial gap by focusing on the detection of product names within unstructured Turkish text. To accomplish this, we propose a deep learning-based NER model which combines a Bidirectional Long Short-Term Memory (BiLSTM) architecture with a Conditional Random Field (CRF) layer, further enhanced by FastText embeddings. To comprehensively evaluate and compare our model’s performance, we explore different embedding approaches, including Word2Vec and Glove, in conjunction with the Bidirectional Long Short-Term Memory and Conditional Random Field (BiLSTM-CRF) model. Furthermore, we conduct comparisons against BERT to assess the efficacy of our approach. Our experimentation utilizes a Turkish e-commerce dataset gathered from the internet, where traditional grammatical and structural rules may not apply. The BiLSTM-CRF model with FastText embeddings achieved an F1 score value of 57.40%, a precision value of 55.78%, and a recall value of 59.12%. These results indicate promising performance in outperforming other baseline techniques. This research contributes to the field of NER by addressing the unique challenges posed by unstructured Turkish text and opens avenues for improved entity recognition in informal language settings, with potential applications across various domains.

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Named entity recognition in Turkish: A comparative study with detailed error analysis

Cited by 9 publications

References 26 publications

State-of-art approach for Indian Language based on NER: Comprehensive Review

State-of-art approach for Indian Language based on NER: Comprehensive Review

Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study (Preprint)

Application of BiLSTM-CRF model with different embeddings for product name extraction in unstructured Turkish text

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