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نتیجه جستجو - Named entity recognition

تعداد مقالات یافته شده: 6
ردیف عنوان نوع
1 Multi-level transfer learning for improving the performance of deep neural networks: Theory and practice from the tasks of facial emotion recognition and named entity recognition
یادگیری انتقال چند سطحی برای بهبود عملکرد شبکه های عصبی عمیق: نظریه و عمل از وظایف تشخیص احساسات چهره و شناسایی موجودیت-2021
Transfer learning has become a promising field in machine learning owing to its wide application prospects. Its effectiveness has spawned various methodologies and practices. Transfer learning refers to improving the performance of target learners in the target domain by transferring the knowledge contained in different yet related source domains. In other words, we can use data from additional domains or tasks to train a model with superior generalization. Using transfer learning, the dependence on considerable target-domain data can be reduced, thereby constructing target learners. Recently, the fields of computer vision (CV) and natural language processing (NLP) have witnessed the emergence of transfer learning, which has significantly improved the most advanced technology on a wide range of CV and NLP tasks. A typical approach of applying transfer learning to deep neural networks is to fine-tune a pretrained model of the source domain with data obtained from the target domain. This paper proposes a novel framework, based on the fine-tuning approach, called multilevel transfer learning (mLTL). Under this framework, we concluded the crucial findings and principles regarding the training sequence of related domain datasets and demonstrated its effectiveness by performing facial emotion and named entity recognition tasks. According to the experimental results, the deep neural network models using mLTL outperformed the original models on the target tasks.© 2021 Elsevier B.V. All rights reserved.
Keywords: Multilevel transfer learning | Computer vision | Natural language processing | Facial emotion recognition | Named entity recognition
مقاله انگلیسی
2 A knowledge graph method for hazardous chemical management: Ontology design and entity identification
یک روش نمودار دانش برای مدیریت مواد شیمیایی خطرناک: طراحی هستی شناسی و شناسایی موجودیت-2021
Hazardous chemicals are widely used in the production activities of the chemical industry. The risk management of hazardous chemicals is critical to the safety of life and property. Hence, the effective risk management of hazardous chemicals has always been important to the chemical industry. Since a large quantity of knowledge and information of hazardous chemicals is stored in isolated databases, it is challenging to manage hazardous chemicals in an information-rich manner. Herein, we prompt a knowledge graph to overcome the information gap between decentralized databases, which would improve the hazardous chemical management. In the implementation of the knowledge graph, we design an ontology schema of hazardous chemicals management. To facilitate enterprises to master the knowledge in the full lifecycle of hazardous chemicals, including production, transportation, storage, etc., we jointly use data from companies and open data from the public domain of hazardous chemicals to construct the knowledge graph. The named entity recognition task is one of the key tasks in the implementation of the knowledge graph, which is of great significance for extracting entity information from unstructured data, namely the hazardous chemical accidents records. To extract useful information from multi-source data, we adopt the pre-trained BERT-CRF model to conduct named entity recognition for incidents records. The model achieves good results, exhibiting the effectiveness in the task of named entity recognition in the chemical industry.
keywords: نمودار دانش | هستی شناسی | مدیریت مواد شیمیایی خطرناک | به رسمیت شناختن نهادها | Knowledge graph | Ontology | Hazardous chemicals management | Named entity recognition
مقاله انگلیسی
3 Data mining for smart legal systems
داده کاوی برای سیستمهای حقوقی هوشمند-2019
Smart legal systems carry immense potential to provide legal community and public with valuable insights using legal data. These systems can consequently help in analyzing and mitigating various social issues. In Pakistan, since last couple of years, courts have been reporting judgments online for public consumption. This public data, once processed, can be utilized for betterment of society and policy making in Pakistan. This study takes the first step to realize smart legal system by extracting various entities such as dates, case numbers, reference cases, person names, etc. from legal judgments. To automatically ex- tract these entities, the primary requirement is to construct dataset using legal judgments. Hence, firstly annotation guidelines are prepared followed by preparation of annotated dataset for extraction of various legal entities. Experiments conducted using variety of datasets, multiple algorithms and annotation schemes, resulted into maximum F1-score of 91.51% using Conditional Random Fields
Keywords: Information extraction | Named Entity Recognition | Legal data | Text mining | Civil law proceeding
مقاله انگلیسی
4 Extracting comprehensive clinical information for breast cancer using deep learning methods
استخراج اطلاعات جامع بالینی برای سرطان پستان با استفاده از روشهای یادگیری عمیق-2019
Objective Breast cancer is the most common malignant tumor among women. The diagnosis and treatment information of breast cancer patients is abundant in multiple types of clinical fields, including clinicopathological data, genotype and phenotype information, treatment information, and prognosis information. However, current studies are mainly focused on extracting information from one specific type of clinical field. This study defines a comprehensive information model to represent the whole-course clinical information of patients. Furthermore, deep learning approaches are used to extract the concepts and their attributes from clinical breast cancer documents by fine-tuning pretrained Bidirectional Encoder Representations from Transformers (BERT) language models. Materials and Methods The clinical corpus that was used in this study was from one 3A cancer hospital in China, consisting of the encounter notes, operation records, pathology notes, radiology notes, progress notes and discharge summaries of 100 breast cancer patients. Our system consists of two components: a named entity recognition (NER) component and a relation recognition component. For each component, we implemented deep learning-based approaches by fine-tuning BERT, which outperformed other state-of-the-art methods on multiple natural language processing (NLP) tasks. A clinical language model is first pretrained using BERT on a large-scale unlabeled corpus of Chinese clinical text. For NER, the context embeddings that were pretrained using BERT were used as the input features of the Bi-LSTM-CRF (Bidirectional long-short-memory-conditional random fields) model and were fine-tuned using the annotated breast cancer notes. Furthermore, we proposed an approach to fine-tune BERT for relation extraction. It was considered to be a classification problem in which the two entities that were mentioned in the input sentence were replaced with their semantic types. Results Our best-performing system achieved F1 scores of 93.53% for the NER and 96.73% for the relation extraction. Additional evaluations showed that the deep learning-based approaches that fine-tuned BERT did outperform the traditional Bi-LSTM-CRF and CRF machine learning algorithms in NER and the attention-Bi-LSTM and SVM (support vector machines) algorithms in relation recognition. Conclusion In this study, we developed a deep learning approach that fine-tuned BERT to extract the breast cancer concepts and their attributes. It demonstrated its superior performance compared to traditional machine learning algorithms, thus supporting its uses in broader NER and relation extraction tasks in the medical domain.
Keywords: clinical information extraction | breast cancer | deep learning | fine-tuning BERT | information model
مقاله انگلیسی
5 From medical records to research papers: A literature analysis pipeline for supporting medical genomic diagnosis processes
از سوابق پزشکی گرفته تا مقاله های تحقیقاتی: یک خط لوله تجزیه و تحلیل ادبیات برای حمایت از فرآیندهای تشخیص ژنومی پزشکی-2019
In this paper, we introduce a framework for processing genetics and genomics literature, based on ontologies and lexical resources from the biomedical domain. The main objective is to support the diagnosis process that is done by medical geneticists who extract knowledge from published works. We constructed a pipeline that gathers several genetics- and genomics-related resources and applies natural language processing techniques, which include named entity recognition and relation extraction. Working on a corpus created from PubMed abstracts, we built a knowledge database that can be used for processing medical records written in Spanish. Given a medical record from Uruguayan healthcare patients, we show how we can map it to the database and perform graph queries for relevant knowledge paths. The framework is not an end user application, but an extensible processing structure to be leveraged by external applications, enabling software developers to streamline incorporation of the extracted knowledge.
Keywords: Controlled vocabulary | Natural language processing | Genomics | Automated pattern recognition | Publications | Medical records
مقاله انگلیسی
6 DISEASES: Text mining and data integration of disease–gene associations
متن کاوی و یکپارچه سازی داده های انجمنی ژن بیماری-2015
Text mining is a flexible technology that can be applied to numerous different tasks in biology and med- icine. We present a system for extracting disease–gene associations from biomedical abstracts. The sys- tem consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease–gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a web interface at http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download.© 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).
Keywords: Text mining | Named entity recognition | Information extraction | Data integration | Web resource
مقاله انگلیسی
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