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Register a new userApplying and Sharing pre-trained BERT-models for Named Entity Recognition and Classification in Swedish Electronic Patient Records
تطبيق وتقاسم نماذج بيرت المدربة مسبقا للتعرف على الكيان المسمى والتصنيف في سجلات المرضى الإلكترونية السويدية
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لتكون قادرا على مشاركة المعلومات القيمة في سجلات المرضى الإلكترونية (EPR)، يلزم تحديدها أولا لحماية خصوصية مواضيعهم. التعرف على الكيان المسمى والتصنيف (NERC) هو جزء مهم من هذه العملية. في السنوات الأخيرة، قامت نماذج اللغة للأغراض العامة المدربة مسبقا على كميات كبيرة من البيانات، ولا سيما بيرت، حققت حالة من النتائج الفنية في NERC، من بين مهام NLP الأخرى. ومع ذلك، حتى الآن، لم يتم إجراء أي محاولات في تطبيق Bert for Nerc على بيانات EPR السويدية. تحاول الدراسة أن تتلقى نماذج برت سويدية ونموذج واحد متعدد اللغات لنموذج NERC على كوربوس EPR السويدية. الهدف من ذلك هو تقييم قابلية تطبيق نماذج بيرت لهذه المهمة وكذلك مقارنة النموذجين في مهمة لغة سويدية خاصة بالمجال. مع النموذج السويدي، يتم استدعاء 0.9220 ودقة 0.9226. هذا تحسن للنتائج السابقة على نفس الكائنات منذ الاستدعاء العالي لا تضحي الدقة. نظرا لأن النماذج تؤدي أيضا بشكل جيد نسبيا عندما يتم ضبطها بشكل جيد مع بيانات مشكرة، فستكون هناك إمكانات جيدة في استخدام هذه الطريقة في نظام قابل للإزالة القابلة للإزالة للنص السريري السويدي.
To be able to share the valuable information in electronic patient records (EPR) they first need to be de-identified in order to protect the privacy of their subjects. Named entity recognition and classification (NERC) is an important part of this process. In recent years, general-purpose language models pre-trained on large amounts of data, in particular BERT, have achieved state of the art results in NERC, among other NLP tasks. So far, however, no attempts have been made at applying BERT for NERC on Swedish EPR data. This study attempts to fine-tune one Swedish BERT-model and one multilingual BERT-model for NERC on a Swedish EPR corpus. The aim is to assess the applicability of BERT-models for this task as well as to compare the two models in a domain-specific Swedish language task. With the Swedish model, recall of 0.9220 and precision of 0.9226 is achieved. This is an improvement to previous results on the same corpus since the high recall does not sacrifice precision. As the models also perform relatively well when fine-tuned with pseudonymised data, it is concluded that there is good potential in using this method in a shareable de-identification system for Swedish clinical text.
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Pretrained language models like BERT have advanced the state of the art for many NLP tasks. For resource-rich languages, one has the choice between a number of language-specific models, while multilingual models are also worth considering. These mode
ls are well known for their crosslingual performance, but have also shown competitive in-language performance on some tasks. We consider monolingual and multilingual models from the perspective of historical texts, and in particular for texts enriched with editorial notes: how do language models deal with the historical and editorial content in these texts? We present a new Named Entity Recognition dataset for Dutch based on 17th and 18th century United East India Company (VOC) reports extended with modern editorial notes. Our experiments with multilingual and Dutch pretrained language models confirm the crosslingual abilities of multilingual models while showing that all language models can leverage mixed-variant data. In particular, language models successfully incorporate notes for the prediction of entities in historical texts. We also find that multilingual models outperform monolingual models on our data, but that this superiority is linked to the task at hand: multilingual models lose their advantage when confronted with more semantical tasks.
Although pre-trained big models (e.g., BERT, ERNIE, XLNet, GPT3 etc.) have delivered top performance in Seq2seq modeling, their deployments in real-world applications are often hindered by the excessive computations and memory demand involved. For ma
ny applications, including named entity recognition (NER), matching the state-of-the-art result under budget has attracted considerable attention. Drawing power from the recent advance in knowledge distillation (KD), this work presents a novel distillation scheme to efficiently transfer the knowledge learned from big models to their more affordable counterpart. Our solution highlights the construction of surrogate labels through the k-best Viterbi algorithm to distill knowledge from the teacher model. To maximally assimilate knowledge into the student model, we propose a multi-grained distillation scheme, which integrates cross entropy involved in conditional random field (CRF) and fuzzy learning.To validate the effectiveness of our proposal, we conducted a comprehensive evaluation on five NER benchmarks, reporting cross-the-board performance gains relative to competing prior-arts. We further discuss ablation results to dissect our gains.
This paper presents our findings from participating in the SMM4H Shared Task 2021. We addressed Named Entity Recognition (NER) and Text Classification. To address NER we explored BiLSTM-CRF with Stacked Heterogeneous embeddings and linguistic feature
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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.
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e complex structures, which fail to learn effective span representations from the input sentence with highly nested entities. Intuitively, explicit span representations will contribute to NNER due to the rich context information they contain. In this study, we propose a Hierarchical Transformer (HiTRANS) network for the NNER task, which decomposes the input sentence into multi-grained spans and enhances the representation learning in a hierarchical manner. Specifically, we first utilize a two-phase module to generate span representations by aggregating context information based on a bottom-up and top-down transformer network. Then a label prediction layer is designed to recognize nested entities hierarchically, which naturally explores semantic dependencies among different spans. Experiments on GENIA, ACE-2004, ACE-2005 and NNE datasets demonstrate that our proposed method achieves much better performance than the state-of-the-art approaches.
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