A simple and fast method for Named Entity context extraction from patents

Puccetti, Giovanni; Chiarello, Filippo; Fantoni, Gualtiero

doi:10.1016/j.eswa.2021.115570

Cited by 13 publications

(2 citation statements)

References 25 publications

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“…To summarize, IE techniques have been successfully implemented in various tasks in the real world, including the extraction of cause-effect relationships [24], event detection and extraction [139], [144], corpus extraction from text [39], [40], [75], KG construction, question answering [36], [99], and mining social media data [148], [150], [157]. The mechanisms of IE from textual data are continually developing as more research is being conducted to address the existing problems within each technique.…”

Section: ) Challenges Based On Rq3: Issues Related To Future Applicat...mentioning

confidence: 99%

Systematic Literature Review of Information Extraction From Textual Data: Recent Methods, Applications, Trends, and Challenges

et al. 2023

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Information extraction (IE) is a challenging task, particularly when dealing with highly heterogeneous data. State-of-the-art data mining technologies struggle to process information from textual data. Therefore, various IE techniques have been developed to enable the use of IE for textual data. However, each technique differs from one another because it is designed for different data types and has different target information to be extracted. This study investigated and described the most contemporary methods for extracting information from textual data, emphasizing their benefits and shortcomings. To provide a holistic view of the domain, this comprehensive systematic literature review employed a systematic mapping process to summarize studies published in the last six years (from 2017 to 2022). It covers fundamental concepts, recent approaches, applications, and trends, in addition to challenges and future research prospects in this domain area. Based on an analysis of 161 selected studies, we found that the state-of-the-art models employ deep learning to extract information from textual data. Finally, this study aimed to guide novice and experienced researchers in future research and serve as a foundation for this research area.

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Section: ) Challenges Based On Rq3: Issues Related To Future Applicat...mentioning

confidence: 99%

Systematic Literature Review of Information Extraction From Textual Data: Recent Methods, Applications, Trends, and Challenges

et al. 2023

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“…That same year, three studies were published that utilized the bidirectional encoder representations from transformers (BERT) architecture [28,43,46]. By 2021 [47][48][49][50][51][52][53][54], the BERT architecture and its variants had emerged as the primary NER model applied to EHRs, a trend that continues to this day [4,24,25,[55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71]. However, this self-attention mechanism was initially introduced in 2017 [72].…”

Section: Classification Modelsmentioning

confidence: 99%

Named Entity Recognition in Electronic Health Records: A Methodological Review

Durango,

Torres-Silva,

Orozco-Duque

2023

Healthc Inform Res

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Objectives: A substantial portion of the data contained in Electronic Health Records (EHR) is unstructured, often appearing as free text. This format restricts its potential utility in clinical decision-making. Named entity recognition (NER) methods address the challenge of extracting pertinent information from unstructured text. The aim of this study was to outline the current NER methods and trace their evolution from 2011 to 2022.Methods: We conducted a methodological literature review of NER methods, with a focus on distinguishing the classification models, the types of tagging systems, and the languages employed in various corpora.Results: Several methods have been documented for automatically extracting relevant information from EHRs using natural language processing techniques such as NER and relation extraction (RE). These methods can automatically extract concepts, events, attributes, and other data, as well as the relationships between them. Most NER studies conducted thus far have utilized corpora in English or Chinese. Additionally, the bidirectional encoder representation from transformers using the BIO tagging system architecture is the most frequently reported classification scheme. We discovered a limited number of papers on the implementation of NER or RE tasks in EHRs within a specific clinical domain.Conclusions: EHRs play a pivotal role in gathering clinical information and could serve as the primary source for automated clinical decision support systems. However, the creation of new corpora from EHRs in specific clinical domains is essential to facilitate the swift development of NER and RE models applied to EHRs for use in clinical practice.

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