Improving Romanian BioNER Using a Biologically Inspired System

Mitrofan, Maria; Pais, V.

doi:10.18653/v1/2022.bionlp-1.30

Cited by 6 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An immediate direction involves delving into the bio-inspired mechanisms, particularly focusing on lateral inhibition mechanisms, given that the BiLSTM architecture, closely mirroring biological neural networks more than transformers, shows promise in reference segmentation tasks. The exploration of reference segmentation through lateral inhibition mechanisms [26] could provide a novel approach, building on the bio-inspired foundations established by the BiLSTM architecture. This could open avenues for enhancing the model's ability to manage the wide variety of bibliographic formats and styles more effectively.…”

Section: Discussionmentioning

confidence: 99%

Neural Architecture Comparison for Bibliographic Reference Segmentation: An Empirical Study

Cuéllar-Hidalgo,

Pinto-Elías,

Torres-Moreno

et al. 2024

Preprint

View full text Add to dashboard Cite

In the realm of digital libraries, efficiently managing and accessing scientific publications necessitates automated bibliographic reference segmentation. This study addresses the challenge of accurately segmenting bibliographic references, a task complicated by the varied formats and styles of references. Focusing on the empirical evaluation of Conditional Random Fields (CRF), Bidirectional Long Short-Term Memory with CRF (BiLSTM+CRF), and Transformer Encoder with CRF (Transformer+CRF) architectures, this research employs Byte Pair Encoding and Character Embeddings for vector representation. The models underwent training on the extensive Giant corpus and subsequent evaluation on the Cora Corpus to ensure a balanced and rigorous comparison, maintaining uniformity across embedding layers, normalization techniques, and Dropout strategies. Results indicate that the BiLSTM+CRF architecture outperforms its counterparts by adeptly handling the syntactic structures prevalent in bibliographic data, achieving an F1-Score of 0.96. This outcome highlights the necessity of aligning model architecture with the specific syntactic demands of bibliographic reference segmentation tasks. Consequently, the study establishes the BiLSTM+CRF model as a superior approach within the current state-of-the-art, offering a robust solution for the challenges faced in digital library management and scholarly communication.

show abstract

Section: Discussionmentioning

confidence: 99%

Neural Architecture Comparison for Bibliographic Reference Segmentation: An Empirical Study

Cuéllar-Hidalgo,

Pinto-Elías,

Torres-Moreno

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…An immediate direction involves delving into the bio-inspired mechanisms, particularly focusing on lateral inhibition mechanisms, given that the BiLSTM architecture, closely mirroring biological neural networks more than transformers, shows promise in reference segmentation tasks. The exploration of reference segmentation through lateral inhibition mechanisms [28] could provide a novel approach, building on the bio-inspired foundations established by the BiLSTM architecture. This could open avenues for enhancing the model's ability to manage the wide variety of bibliographic formats and styles more effectively.…”

Section: Discussionmentioning

confidence: 99%

Neural Architecture Comparison for Bibliographic Reference Segmentation: An Empirical Study

Cuéllar Hidalgo,

Pinto Elías,

Torres-Moreno

et al. 2024

Data

View full text Add to dashboard Cite

In the realm of digital libraries, efficiently managing and accessing scientific publications necessitates automated bibliographic reference segmentation. This study addresses the challenge of accurately segmenting bibliographic references, a task complicated by the varied formats and styles of references. Focusing on the empirical evaluation of Conditional Random Fields (CRF), Bidirectional Long Short-Term Memory with CRF (BiLSTM + CRF), and Transformer Encoder with CRF (Transformer + CRF) architectures, this research employs Byte Pair Encoding and Character Embeddings for vector representation. The models underwent training on the extensive Giant corpus and subsequent evaluation on the Cora Corpus to ensure a balanced and rigorous comparison, maintaining uniformity across embedding layers, normalization techniques, and Dropout strategies. Results indicate that the BiLSTM + CRF architecture outperforms its counterparts by adeptly handling the syntactic structures prevalent in bibliographic data, achieving an F1-Score of 0.96. This outcome highlights the necessity of aligning model architecture with the specific syntactic demands of bibliographic reference segmentation tasks. Consequently, the study establishes the BiLSTM + CRF model as a superior approach within the current state-of-the-art, offering a robust solution for the challenges faced in digital library management and scholarly communication.

show abstract

“…In an extremely low-resource setting, the Japanese medical UTH-BERT [60] provided benefits only for radiology reports, owing to their linguistic similarity to the training data; for other cases, the general BERT model produced more favorable results [29]. These models have also demonstrated their effectiveness in languages such as Portuguese [61] and Romanian [62]. Kepler et al conducted a comprehensive evaluation of state-of-the-art NER methods on Serbian clinical narratives and found that combining existing models in a majority voting ensemble produced the best F1 score of 89.2%, showcasing the potential of hybrid approaches [63].…”

Section: Named Entity Recognition Normalization and Linking For Loementioning

confidence: 99%

Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey

Shaitarova,

Zaghir,

Lavelli

et al. 2023

Yearb Med Inform

View full text Add to dashboard Cite

Objectives: This survey aims to provide an overview of the current state of biomedical and clinical Natural Language Processing (NLP) research and practice in Languages other than English (LoE). We pay special attention to data resources, language models, and popular NLP downstream tasks. Methods: We explore the literature on clinical and biomedical NLP from the years 2020-2022, focusing on the challenges of multilinguality and LoE. We query online databases and manually select relevant publications. We also use recent NLP review papers to identify the possible information lacunae. Results: Our work confirms the recent trend towards the use of transformer-based language models for a variety of NLP tasks in medical domains. In addition, there has been an increase in the availability of annotated datasets for clinical NLP in LoE, particularly in European languages such as Spanish, German and French. Common NLP tasks addressed in medical NLP research in LoE include information extraction, named entity recognition, normalization, linking, and negation detection. However, there is still a need for the development of annotated datasets and models specifically tailored to the unique characteristics and challenges of medical text in some of these languages, especially low-resources ones. Lastly, this survey highlights the progress of medical NLP in LoE, and helps at identifying opportunities for future research and development in this field.

show abstract

Improving Romanian BioNER Using a Biologically Inspired System

Cited by 6 publications

References 0 publications

Neural Architecture Comparison for Bibliographic Reference Segmentation: An Empirical Study

Neural Architecture Comparison for Bibliographic Reference Segmentation: An Empirical Study

Neural Architecture Comparison for Bibliographic Reference Segmentation: An Empirical Study

Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey

Contact Info

Product

Resources

About