Named Entity Recognition Over Electronic Health Records Through a Combined Dictionary-based Approach

Quimbaya, Alexandra Pomares; Múnera, Alejandro Sierra; Rivera, Rafael Andrés González; Rodríguez, Julián Camilo Daza; Velandía, Oscar Mauricio Muñoz; Peña, Ángel Alberto García; Labbé, Cyril

doi:10.1016/j.procs.2016.09.123

Cited by 89 publications

(33 citation statements)

References 13 publications

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“…In biomedical domain, Hanisch et al [43] proposed ProMiner, which leverages a pre-processed synonym dictionary to identify protein mentions and potential gene in biomedical text. Quimbaya et al [44] proposed a dictionary-based approach for NER in electronic health records. Experimental results show the approach improves recall while having limited impact on precision.…”

Section: Rule-based Approachesmentioning

confidence: 99%

A Survey on Deep Learning for Named Entity Recognition

Sun

Han³

et al. 2022

IEEE Trans. Knowl. Data Eng.

877

397

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Named entity recognition (NER) is the task to identify text spans that mention named entities, and to classify them into predefined categories such as person, location, organization etc. NER serves as the basis for a variety of natural language applications such as question answering, text summarization, and machine translation. Although early NER systems are successful in producing decent recognition accuracy, they often require much human effort in carefully designing rules or features. In recent years, deep learning, empowered by continuous real-valued vector representations and semantic composition through nonlinear processing, has been employed in NER systems, yielding stat-of-the-art performance. In this paper, we provide a comprehensive review on existing deep learning techniques for NER. We first introduce NER resources, including tagged NER corpora and off-the-shelf NER tools. Then, we systematically categorize existing works based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder. Next, we survey the most representative methods for recent applied techniques of deep learning in new NER problem settings and applications. Finally, we present readers with the challenges faced by NER systems and outline future directions in this area.

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Section: Rule-based Approachesmentioning

confidence: 99%

A Survey on Deep Learning for Named Entity Recognition

Sun

Han³

et al. 2022

IEEE Trans. Knowl. Data Eng.

877

397

View full text Add to dashboard Cite

show abstract

“…The creators in [6] makes reference to Conditional Random Fields (CRF), Support Vector Machines (SVM) and Hidden Markov Model (HMM) as regular AI strategies that are at present applied for NER undertakings in clinical space. The latest papers focus on profound wisdom methodologies put on repetitive neural systems (RNNs), for example, Long-Short Term Memory (LSTM) [7], Gated Recurrent Units (GRU) [8]. Basic pattern is joining the RNN with factual technique on head of the intermittent layers.…”

Section: Background Studymentioning

confidence: 99%

Named Entity Recognition using Ensemble Learning

Ramachandran¹,

Arutchelvan²,

Selvan³

2020

IRJASH

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Upgrading Industry 4.0 to 5.0 provides numerous research opportunities for the industrialists and researchers. This industrial revolution cross the peak of automation in the life science domain. In this digitalized world, big data plays a key role to provide the valuable insights by using various analytical methods. In life science, available of huge textual data contains wide spread of valuable information. To extract the hidden information from the big data, natural language processing plays a major and significant role. In NLP, named entity recognition is one of the key factor and biggest challenge for the research community. This paper presents the high level architecture of NER using ensemble learning method. The EL model contains a dictionary based entity identifier and a self-learning classifier. Proposed model outperformed well and produced high accuracy.

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“…Shaalan (2010) proposed the idea of a local grammar combined with a set of names for identifying entities with Arabic names. Quimbaya et al, (2016) defined a set of dictionaries for extracting named entities from electronic free health records. Riaz (2010) defined a detailed set of rules and patterns for Urdu NER to address issues such as the agglutinative nature of the language, lack of capitalization, and spelling variations.…”

Section: Rule-based Approachesmentioning

confidence: 99%

“…In their method, many heuristics and grammatical rules were constructed for identifying various classes, including the prices of some drugs, types of drugs, and numbers of drugs. Quimbaya et al, (2016) defined a set of dictionaries for extracting named entities from electronic free health records. They applied stemmed matching and fuzzy matching approaches to identify relevant named entities such as treatments and diagnoses.…”

Section: Rule-based Approachesmentioning

confidence: 99%

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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Named Entity Recognition Over Electronic Health Records Through a Combined Dictionary-based Approach

Cited by 89 publications

References 13 publications

A Survey on Deep Learning for Named Entity Recognition

A Survey on Deep Learning for Named Entity Recognition

Named Entity Recognition using Ensemble Learning

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

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