Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA Comparison between Named Entity Recognition Models in the Biomedical Domain
مقارنة بين نماذج التعرف على الكيانات المسماة في المجال الطبي الطبيعي
729
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
يعرف تطبيق المتخصص في مجال التعرف على الكيان المسمى (ner) باسم الطيوميدي (Bioner)، الذي يهدف إلى تحديد وتصنيف المفاهيم الطبية الحيوية التي تهم الباحثين، مثل الجينات والبروتينات والمركبات الكيميائية والمخدرات والطفرات والأمراض ، وما إلى ذلك وهلم جرا. تشبه المهمة Bioner إلى حد كبير عام NER ولكن الاعتراف بالكيانات الطبية الحيوية المسماة (BNES) أكثر تحديا من الاعتراف بالأسماء المناسبة من الصحف بسبب خصائص التسميات الطبية الحيوية. من أجل معالجة التحديات التي تطرحها Bioner، تم تنفيذ سبع نماذج تعليمية في سبع آلات مقارنة نهج تعلم النقل بناء على برت ضبط صقل مع النماذج العصبية التي تعتمد عليها BI-LSTM وطراز CRF يستخدم كناسما أساسيا. تم استخدام الدقة والتذكر و F1-Score نظرا لأن درجات الأداء التي تقوم بتقييم النماذج على اثنين من الأطباق الطبية الحيوية المعروفة: Jnlpba و Biocreative IV (BC-IV). تعتبر مطابقة صارمة جزئية معايير تقييم. تشير النتائج المبلغ عنها إلى أن نهج التعلم عبر النقل يعتمد على بيرت المصنفات التي تم ضبطها تتفوق جميع أساليب الآخرين على تحقيق أعلى درجات لجميع المقاييس على كلا الشريعة.
The domain-specialised application of Named Entity Recognition (NER) is known as Biomedical NER (BioNER), which aims to identify and classify biomedical concepts that are of interest to researchers, such as genes, proteins, chemical compounds, drugs, mutations, diseases, and so on. The BioNER task is very similar to general NER but recognising Biomedical Named Entities (BNEs) is more challenging than recognising proper names from newspapers due to the characteristics of biomedical nomenclature. In order to address the challenges posed by BioNER, seven machine learning models were implemented comparing a transfer learning approach based on fine-tuned BERT with Bi-LSTM based neural models and a CRF model used as baseline. Precision, Recall and F1-score were used as performance scores evaluating the models on two well-known biomedical corpora: JNLPBA and BIOCREATIVE IV (BC-IV). Strict and partial matching were considered as evaluation criteria. The reported results show that a transfer learning approach based on fine-tuned BERT outperforms all others methods achieving the highest scores for all metrics on both corpora.
References used
https://aclanthology.org/
rate research
Read More
Named entity disambiguation (NED), which involves mapping textual mentions to structured entities, is particularly challenging in the medical domain due to the presence of rare entities. Existing approaches are limited by the presence of coarse-grain
ed structural resources in biomedical knowledge bases as well as the use of training datasets that provide low coverage over uncommon resources. In this work, we address these issues by proposing a cross-domain data integration method that transfers structural knowledge from a general text knowledge base to the medical domain. We utilize our integration scheme to augment structural resources and generate a large biomedical NED dataset for pretraining. Our pretrained model with injected structural knowledge achieves state-of-the-art performance on two benchmark medical NED datasets: MedMentions and BC5CDR. Furthermore, we improve disambiguation of rare entities by up to 57 accuracy points.
The number of biomedical documents is increasing rapidly. Accordingly, a demand for extracting knowledge from large-scale biomedical texts is also increasing. BERT-based models are known for their high performance in various tasks. However, it is oft
en computationally expensive. A high-end GPU environment is not available in many situations. To attain both high accuracy and fast extraction speed, we propose combinations of simpler pre-trained models. Our method outperforms the latest state-of-the-art model and BERT-based models on the GAD corpus. In addition, our method shows approximately three times faster extraction speed than the BERT-based models on the ChemProt corpus and reduces the memory size to one sixth of the BERT ones.
Cross-domain Named Entity Recognition (NER) transfers the NER knowledge from high-resource domains to the low-resource target domain. Due to limited labeled resources and domain shift, cross-domain NER is a challenging task. To address these challeng
es, we propose a progressive domain adaptation Knowledge Distillation (KD) approach -- PDALN. It achieves superior domain adaptability by employing three components: (1) Adaptive data augmentation techniques, which alleviate cross-domain gap and label sparsity simultaneously; (2) Multi-level Domain invariant features, derived from a multi-grained MMD (Maximum Mean Discrepancy) approach, to enable knowledge transfer across domains; (3) Advanced KD schema, which progressively enables powerful pre-trained language models to perform domain adaptation. Extensive experiments on four benchmarks show that PDALN can effectively adapt high-resource domains to low-resource target domains, even if they are diverse in terms and writing styles. Comparison with other baselines indicates the state-of-the-art performance of PDALN.
Current work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models. However, most existing techniques focus on augmenting in-domain data in low-resource scenarios where annotated data is quite limite
d. In this work, we take this research direction to the opposite and study cross-domain data augmentation for the NER task. We investigate the possibility of leveraging data from high-resource domains by projecting it into the low-resource domains. Specifically, we propose a novel neural architecture to transform the data representation from a high-resource to a low-resource domain by learning the patterns (e.g. style, noise, abbreviations, etc.) in the text that differentiate them and a shared feature space where both domains are aligned. We experiment with diverse datasets and show that transforming the data to the low-resource domain representation achieves significant improvements over only using data from high-resource domains.
Recognition of named entities present in text is an important step towards information extraction and natural language understanding. This work presents a named entity recognition system for the Romanian legal domain. The system makes use of the gold
annotated LegalNERo corpus. Furthermore, the system combines multiple distributional representations of words, including word embeddings trained on a large legal domain corpus. All the resources, including the corpus, model and word embeddings are open sourced. Finally, the best system is available for direct usage in the RELATE platform.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
