Recent Named Entity Recognition and Classification techniques: A systematic review

Goyal, Archana; Gupta, Vishal; Kumar, Manish

doi:10.1016/j.cosrev.2018.06.001

Cited by 212 publications

(115 citation statements)

References 77 publications

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“…The key goal of an unsupervised learning method is to produce a model that fully captures the distributional and structural features of the data to achieve better learning (Goyal et al, 2018). These systems/methods apply only unlabeled data for decision making.…”

Section: Unsupervised Learningmentioning

confidence: 99%

“…These systems/methods apply only unlabeled data for decision making. The key goal of an unsupervised learning method is to produce a model that fully captures the distributional and structural features of the data to achieve better learning (Goyal et al, 2018).…”

Section: Unsupervised Learningmentioning

confidence: 99%

“…However, existing techniques and models for automatic GNER and classification are limited in their capabilities compared to other NER efforts involving geological reports for three reasons (Goyal et al, 2018;Liu & El-Gohary, 2017;Zhou & El-Gohary, 2017). Then, the extracted important information should be transformed into a structured form to enable further flexible analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Then, the extracted important information should be transformed into a structured form to enable further flexible analysis. However, existing techniques and models for automatic GNER and classification are limited in their capabilities compared to other NER efforts involving geological reports for three reasons (Goyal et al, 2018;Liu & El-Gohary, 2017;Zhou & El-Gohary, 2017). First, compared to domain-general narrative text, geological reports are highly ambiguous and variable, in terms of both their textual characteristics and patterns because these reports are typically written by different inspectors/experts from a variety of scientific organizations and governmental agencies.…”

Section: Introductionmentioning

confidence: 99%

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GNER: A Generative Model for Geological Named Entity Recognition Without Labeled Data Using Deep Learning

Qiu

Xie

Tao

2019

Earth and Space Science

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A variety of detailed data about geological topics and geoscience knowledge are buried in the geoscience literature and rarely used. Named entity recognition (NER) provides both opportunities and challenges to leverage this wealth of data in the geoscience literature for data analysis and further information extraction. Existing NER models and techniques are mainly based on rule‐based and supervised approaches, and developing such systems requires a costly manual effort. In this paper, we first design a generic stepwise framework for domain‐specific NER. Following this framework, domain‐specific entities and domain‐general words are collected and selected as seed terms. Normalization and grouping processes are then applied to these seed terms for further analysis. A random extraction algorithm based on a unigram language model is used to generate a large‐scale training data set consisting of probabilistically labeled pseudosentences. Each generated sentence is then used as input to the self‐training and learning algorithm. Experimental results on two constructed data sets demonstrate that the proposed model effectively recognizes and identifies geological named entities.

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Section: Unsupervised Learningmentioning

confidence: 99%

Section: Unsupervised Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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GNER: A Generative Model for Geological Named Entity Recognition Without Labeled Data Using Deep Learning

Qiu

Xie

Tao

2019

Earth and Space Science

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“…Supervised learning has been extensively used in extensive research works for NER [45][46][47][48] as an alternative to classic adaptations of probabilistic language models, such as conditional random fields and hidden Markov models. In this section, we present an algorithm based on random forests to create a classification model to address the sensor design.…”

Section: Supervised Learningmentioning

confidence: 99%

Sensing Earthquake Disaster Information: A Named Entity Recognition Approach Using Twitter Collaborative Data

Hernandez-Suarez¹,

Sánchez-Pérez²,

Toscano-Medina³

et al. 2018

Preprint

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Abstract:In recent years, online social networks have received important consideration in spatial modelling fields given the critical information that can be extracted from them for events in real time; one of the most latent issues is that regarding various natural disasters such as earthquakes. Although it is possible to retrieve data from these social networks with embedded geographic information provided by GPS, in many cases this is not possible. An alternative solution is to reconstruct specific locations using probabilistic language models, more specifically those based on Name Entity Recognition (NER), which extracts names from a user's description about an event occurring in a specific place (e.g., a collapsed building on a specific avenue). In this work, we present a methodology to use twitter as a social sensor system for disasters. The methodology scores NER locations with a kernel density estimation function for different subtopics originating from a natural disaster and that maps them into a geographic space is proposed. The proposed methodology is evaluated with tweets related to the 2017 earthquake in Mexico.

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Development and Validation of a Model to Identify Critical Brain Injuries Using Natural Language Processing of Text Computed Tomography Reports

et al. 2022

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IMPORTANCE Clinical text reports from head computed tomography (CT) represent rich, incompletely utilized information regarding acute brain injuries and neurologic outcomes. CT reports are unstructured; thus, extracting information at scale requires automated natural language processing (NLP). However, designing new NLP algorithms for each individual injury category is an unwieldy proposition. An NLP tool that summarizes all injuries in head CT reports would facilitate exploration of large data sets for clinical significance of neuroradiological findings. OBJECTIVETo automatically extract acute brain pathological data and their features from head CT reports. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study developed a 2-part named entity recognition (NER) NLP model to extract and summarize data on acute brain injuries from head CT reports. The model, termed BrainNERD, extracts and summarizes detailed brain injury information for research applications. Model development included building and comparing 2 NER models using a custom dictionary of terms, including lesion type, location, size, and age, then designing a rulebased decoder using NER outputs to evaluate for the presence or absence of injury subtypes. BrainNERD was evaluated against independent test data sets of manually classified reports, including 2 external validation sets. The model was trained on head CT reports from 1152 patients generated by neuroradiologists at the Yale Acute Brain Injury Biorepository. External validation was conducted using reports from 2 outside institutions. Analyses were conducted from May 2020 to December 2021. MAIN OUTCOMES AND MEASURES Performance of the BrainNERD model was evaluated using precision, recall, and F1 scores based on manually labeled independent test data sets. RESULTS A total of 1152 patients (mean [SD] age, 67.6 [16.1] years; 586 [52%] men), were included in the training set. NER training using transformer architecture and bidirectional encoderrepresentations from transformers was significantly faster than spaCy. For all metrics, the 10-fold cross-validation performance was 93% to 99%. The final test performance metrics for the NER test data set were 98.82% (95% CI, 98.37%-98.93%) for precision, 98.81% (95% CI, 98.46%-99.06%) for recall, and 98.81% (95% CI, 98.40%-98.94%) for the F score. The expert review comparison metrics were 99.06% (95% CI, 97.89%-99.13%) for precision, 98.10% (95% CI, 97.93%-98.77%) for recall, and 98.57% (95% CI, 97.78%-99.10%) for the F score. The decoder test set metrics were 96.06% (95% CI, 95.01%-97.16%) for precision, 96.42% (95% CI, 94.50%-97.87%) for recall, and 96.18% (95% CI, 95.151%-97.16%) for the F score. Performance in external institution report validation including 1053 head CR reports was greater than 96%. (continued) Key Points Question Can a named entity recognition (NER) natural language processing and decoding model extract information about all acute brain injuries described in the text reports of computed tomography head scans? Findings This diagnostic stud...

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Recent Named Entity Recognition and Classification techniques: A systematic review

Cited by 212 publications

References 77 publications

GNER: A Generative Model for Geological Named Entity Recognition Without Labeled Data Using Deep Learning

GNER: A Generative Model for Geological Named Entity Recognition Without Labeled Data Using Deep Learning

Sensing Earthquake Disaster Information: A Named Entity Recognition Approach Using Twitter Collaborative Data

Development and Validation of a Model to Identify Critical Brain Injuries Using Natural Language Processing of Text Computed Tomography Reports

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