Research on Named Entity Recognition Based on Text Data of Beijing Subway Accident

2021 ◽  
Author(s):  
Jiming Zuo ◽  
Keping Li
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Text Data ◽  
Named Entity ◽  
Beijing Subway

scholarly journals New approach for Arabic named entity recognition on social media based on feature selection using genetic algorithm

2021 ◽  
Vol 11 (2) ◽  
pp. 1485
Author(s):  
Brahim Ait Benali ◽  
Soukaina Mihi ◽  
Ismail El Bazi ◽  
Nabil Laachfoubi
Keyword(s):  
Genetic Algorithm ◽  
Social Media ◽  
Feature Selection ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Support Vector ◽  
Text Data ◽  
Impact Performance ◽  
Named Entity ◽  
Feature Selection Approach

Many features can be extracted from the massive volume of data in different types that are available nowadays on social media. The growing demand for multimedia applications was an essential factor in this regard, particularly in the case of text data. Often, using the full feature set for each of these activities can be time-consuming and can also negatively impact performance. It is challenging to find a subset of features that are useful for a given task due to a large number of features. In this paper, we employed a feature selection approach using the genetic algorithm to identify the optimized feature set. Afterward, the best combination of the optimal feature set is used to identify and classify the Arabic named entities (NEs) based on support vector. Experimental results show that our system reaches a state-of-the-art performance of the Arab NER on social media and significantly outperforms the previous systems.

scholarly journals ABioNER: A BERT-Based Model for Arabic Biomedical Named-Entity Recognition

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Nada Boudjellal ◽  
Huaping Zhang ◽  
Asif Khan ◽  
Arshad Ahmad ◽  
Rashid Naseem ◽  
...  
Keyword(s):  
Named Entity Recognition ◽  
Model Performance ◽  
Recognition Task ◽  
Entity Recognition ◽  
Small Scale ◽  
Text Data ◽  
Named Entities ◽  
Named Entity ◽  
Textual Data ◽  
Biomedical Named Entity Recognition

The web is being loaded daily with a huge volume of data, mainly unstructured textual data, which increases the need for information extraction and NLP systems significantly. Named-entity recognition task is a key step towards efficiently understanding text data and saving time and effort. Being a widely used language globally, English is taking over most of the research conducted in this field, especially in the biomedical domain. Unlike other languages, Arabic suffers from lack of resources. This work presents a BERT-based model to identify biomedical named entities in the Arabic text data (specifically disease and treatment named entities) that investigates the effectiveness of pretraining a monolingual BERT model with a small-scale biomedical dataset on enhancing the model understanding of Arabic biomedical text. The model performance was compared with two state-of-the-art models (namely, AraBERT and multilingual BERT cased), and it outperformed both models with 85% F1-score.

scholarly journals NETANOS - Named entity-based Text Anonymization for Open Science

2017 ◽  
Author(s):  
Bennett Kleinberg ◽  
Maximilian Mozes ◽  
Yaloe van der Toolen ◽  
Bruno Verschuere
Keyword(s):  
Contextual Information ◽  
Named Entity Recognition ◽  
Open Science ◽  
Entity Recognition ◽  
Text Data ◽  
Named Entities ◽  
Fast System ◽  
Named Entity ◽  
Unstructured Text ◽  
Science Movement

Background: The shift towards open science, implies that researchers should share their data. Often there is a dilemma between publicly sharing data and protecting their subjects' confidentiality. Moreover, the case of unstructured text data (e.g. stories) poses an additional dilemma: anonymizing texts without deteriorating their content for secondary research. Existing text anonymization systems either deteriorate the content of the original or have not been tested empirically. We propose and empirically evaluate NETANOS: named entity-based text anonymization for open science. NETANOS is an open-source context-preserving anonymization system that identifies and modifies named entities (e.g. persons, locations, times, dates). The aim is to assist researchers in sharing their raw text data.Method & Results: NETANOS anonymizes critical, contextual information through a stepwise named entity recognition (NER) implementation: it identifies contextual information (e.g. "Munich") and then replaces them with a context-preserving category label (e.g. "Location_1"). We assessed how good participants were in re-identifying several travel stories (e.g. locations, names) that were presented in the original (“Max”), human anonymized (“Max” → “Person1”), NETANOS (”Max” → “Person1”), and in a context-deteriorating state (“Max” → “XXX”). Bayesian testing revealed that the NETANOS anonymization was practically equivalent to the human baseline anonymization.Conclusions: Named entity recognition can be applied to the anonymization of critical, identifiable information in text data. The proposed stepwise anonymization procedure provides a fully automated, fast system for text anonymization. NETANOS might be an important step to address researchers' dilemmas when sharing text data within the open science movement.

scholarly journals Study on structured method of Chinese MRI report of nasopharyngeal carcinoma

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Xin Huang ◽  
Hui Chen ◽  
Jing-Dong Yan
Keyword(s):  
High Performance ◽  
Nasopharyngeal Cancer ◽  
Named Entity Recognition ◽  
Text Structure ◽  
Entity Recognition ◽  
Knowledge Network ◽  
Medical Field ◽  
Text Data ◽  
Named Entity ◽  
Extraction Model

Abstract Background Image text is an important text data in the medical field at it can assist clinicians in making a diagnosis. However, due to the diversity of languages, most descriptions in the image text are unstructured data. The same medical phenomenon may also be described in various ways, such that it remains challenging to conduct text structure analysis. The aim of this research is to develop a feasible approach that can automatically convert nasopharyngeal cancer reports into structured text and build a knowledge network. Methods In this work, we compare commonly used named entity recognition (NER) models, choose the optimal model as our triplet extraction model, and present a Chinese structuring algorithm. Finally, we visualize the results of the algorithm in the form of a knowledge network of nasopharyngeal cancer. Results In NER, both accuracy and recall of the BERT-CRF model reached 99%. The structured extraction rate is 84.74%, and the accuracy is 89.39%. The architecture based on recurrent neural network does not rely on medical dictionaries or word segmentation tools and can realize triplet recognition. Conclusions The BERT-CRF model has high performance in NER, and the triplet can reflect the content of the image report. This work can provide technical support for the construction of a nasopharyngeal cancer database.

Developing a RadLex-based Named Entity Recognition Tool for Mining Textual Radiology Reports (Preprint)

2020 ◽  
Author(s):  
Shintaro Tsuji ◽  
Andrew Wen ◽  
Naoki Takahashi ◽  
Hongjian Zhang ◽  
Katsuhiko Ogasawara ◽  
...  
Keyword(s):  
Named Entity Recognition ◽  
Noun Phrases ◽  
General Purpose ◽  
Entity Recognition ◽  
Free Text ◽  
Clinical Text ◽  
Named Entity ◽  
Radiology Reports ◽  
Two Measures ◽  
F Measure

BACKGROUND Named entity recognition (NER) plays an important role in extracting the features of descriptions for mining free-text radiology reports. However, the performance of existing NER tools is limited because the number of entities depends on its dictionary lookup. Especially, the recognition of compound terms is very complicated because there are a variety of patterns. OBJECTIVE The objective of the study is to develop and evaluate a NER tool concerned with compound terms using the RadLex for mining free-text radiology reports. METHODS We leveraged the clinical Text Analysis and Knowledge Extraction System (cTAKES) to develop customized pipelines using both RadLex and SentiWordNet (a general-purpose dictionary, GPD). We manually annotated 400 of radiology reports for compound terms (Cts) in noun phrases and used them as the gold standard for the performance evaluation (precision, recall, and F-measure). Additionally, we also created a compound-term-enhanced dictionary (CtED) by analyzing false negatives (FNs) and false positives (FPs), and applied it for another 100 radiology reports for validation. We also evaluated the stem terms of compound terms, through defining two measures: an occurrence ratio (OR) and a matching ratio (MR). RESULTS The F-measure of the cTAKES+RadLex+GPD was 32.2% (Precision 92.1%, Recall 19.6%) and that of combined the CtED was 67.1% (Precision 98.1%, Recall 51.0%). The OR indicated that stem terms of “effusion”, "node", "tube", and "disease" were used frequently, but it still lacks capturing Cts. The MR showed that 71.9% of stem terms matched with that of ontologies and RadLex improved about 22% of the MR from the cTAKES default dictionary. The OR and MR revealed that the characteristics of stem terms would have the potential to help generate synonymous phrases using ontologies. CONCLUSIONS We developed a RadLex-based customized pipeline for parsing radiology reports and demonstrated that CtED and stem term analysis has the potential to improve dictionary-based NER performance toward expanding vocabularies.

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