Entity Recognition Approach of Clinical Documents Based on Self-training Framework

Che, Nannan; Chen, Dehua; Le, Jiajin

doi:10.1007/978-981-10-8944-2_31

Cited by 2 publications

(4 citation statements)

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“…It has been identified that text ambiguity, lack of resources, complex nested entities, identification of contextual information, noise in the form of homonyms, language variability and missing data are important challenges in entity recognition from unstructured big data [11,16,105]. It is also found that the volume of unstructured big data changed the technological paradigm from traditional rule-based or learning-based techniques to [9,10].…”

Section: Named Entity Recognition (Ner)mentioning

confidence: 99%

“…Supervised techniques require manually labeled training data which is one of the major drawbacks of these techniques. Large scale labeled corpus construction is laborious and time consuming task [9]. These techniques are effective for domain-specific IE where specific information is required to be extracted.…”

Section: State-of-the-art Ie Techniquesmentioning

confidence: 99%

“…Interpretable and suitable for rapid development and domain transfer [114] The performance of machine learning approaches is better in terms of precision and recall but appropriate feature selection is important [115] Humans and machines can contribute to the same model. So it is easy to incorporate domain knowledge [114] Heavily rely on domain thesauri [11] Generating training data is time consuming in learningbased approaches whereas rule-based approaches require pre-defined vocabularies [116] Although rule-based systems require domain knowledge and are time consuming, results proved that these are more reliable and useful for automated processing [117] No experts are required and system can be developed quickly with relatively low cost [118] Declarative [119] Adaptable [119] Requires tiresome manual work [118] Less manual effort [118] Highly transparent and expressive Higher portability than rule-based [9] of these techniques also depends on the selected features like morphological, syntactic, semantic and lexical features. Whereas, unsupervised IE techniques do not need labeled data.…”

Section: Rule-based Approaches Learning-based Approachesmentioning

confidence: 99%

“…Semi-supervised techniques use both labeled and unlabeled corpus with small degree of supervision [121]. For large scale data, distant supervised learning [26], deep learning (CNN, RNN, DNN) [9,10,18,23,[31][32][33], transfer learning [25] techniques are more suitable for IE from free-text data.…”

Section: Rule-based Approaches Learning-based Approachesmentioning

confidence: 99%

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An analytical study of information extraction from unstructured and multidimensional big data

2019

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IntroductionInformation extraction (IE) process extracts useful structured information from the unstructured data in the form of entities, relations, objects, events and many other types. The extracted information from unstructured data is used to prepare data for analysis. Therefore, the efficient and accurate transformation of unstructured data in the IE process improves the data analysis. Numerous techniques have been introduced for different data types i.e. text, image, audio, and video.The advancement in technology promoted the rapid growth of data volume in recent years. The volume, variety (structured, unstructured, and semi-structured data) and velocity of big data have also changed the paradigm of computational capabilities of the systems. IBM estimated that more than 2.5 quintillion bytes of data are generated every Abstract Process of information extraction (IE) is used to extract useful information from unstructured or semi-structured data. Big data arise new challenges for IE techniques with the rapid growth of multifaceted also called as multidimensional unstructured data. Traditional IE systems are inefficient to deal with this huge deluge of unstructured big data. The volume and variety of big data demand to improve the computational capabilities of these IE systems. It is necessary to understand the competency and limitations of the existing IE techniques related to data pre-processing, data extraction and transformation, and representations for huge volumes of multidimensional unstructured data. Numerous studies have been conducted on IE, addressing the challenges and issues for different data types such as text, image, audio and video. Very limited consolidated research work have been conducted to investigate the task-dependent and task-independent limitations of IE covering all data types in a single study. This research work address this limitation and present a systematic literature review of state-of-the-art techniques for a variety of big data, consolidating all data types. Recent challenges of IE are also identified and summarized. Potential solutions are proposed giving future research directions in big data IE. The research is significant in terms of recent trends and challenges related to big data analytics. The outcome of the research and recommendations will help to improve the big data analytics by making it more productive. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Adnan and Akbar J Big Data (2019) 6:91 Malaysia Adnan and Akbar J Big Data (2019) 6:91 RESEARCHday. Among these statistics, it was also predicted that unstructured data from diverse sources will grow up to 90% in few years. IDC estimated that unstructured data will be 95% of the global data in 2020 with estimated 65% annual growth rate [1]. The common characteristics of unstructured data are, (i) it comes in multiple formats...

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