Neural Text Classification and Stacked Heterogeneous Embeddings for Named Entity Recognition in SMM4H 2021

Yaseen, Usama; Langer, Stefan

doi:10.48550/arxiv.2106.05823

Cited by 2 publications

(4 citation statements)

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“…The current selection is a step towards achieving accuracy in the English language NER, which is closer to humans than many consider possible. In this light, the current choice to create a common model for each English language NER is triggered [16]. To further develop a model, which can be applied to the NER of some other languages, as stated below with minor modification.…”

Section: Problem Definitionmentioning

confidence: 99%

“…Moreover, these operators (S and A) can effortlessly method the board the largest because of their little dispersal different variables. The exploitation phase is mathematically presented as follows, X 1,j (C. Iter + 1) = { best(X j ) − (MOP) × (ub j − lb j ) × μ + lb j ),r3 < 0.5 best(X j ) + (MOP) × (ub j − lb j ) × μ + lb j ),otherwise (16) This stage exploits the search space by managing the deep search. The initial operator (S), in this phase is managed by 𝑟3 > 0.5 in addition the remaining operator (A) will be deserted until this operative completes its present action.…”

Section: Exploitation Stagementioning

confidence: 99%

“…In this light, the current choice to create a common model for each English language NER is triggered. 21 To further develop a model, which can be applied to the NER of some other languages, as stated below with minor modification. The basic challenges are introduced as follows, Some of them are:…”

Section: Problem Definitionmentioning

confidence: 99%

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Enhanced conditional random field‐long short‐term memory for name entity recognition in English texts

Bhumireddypalli¹,

Koppula²,

Koppula³

2023

Concurrency and Computation

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Named Entity recognition (NER) is the essential topic in the real world during the advanced development of technologies. Hence, in this paper, to develop Enhanced Conditional Random Field-Long Short-Term Memory (ECRF-LSTM) for NER in English language. The proposed ECRF-LSTM is combination of Conditional Random Field-Long Short-Term Memory (ECRF-LSTM) and Arithmetic Optimization Algorithm (AOA). This proposed method is utilizing to NER from the English texts. The proposed method is working with three phases such as preprocessing phase, feature extraction phase, and NER phase. Initially, the datasets are collected from the online system. In the pre-processing phase, removal of URL, removal of special symbol, username removal, tokenization and stop word removal are done. After that, the essential features such as domain weight, event weight, textual similarity, spatial similarity, temporal similarity, and Relative Document-Term Frequency Difference (RDTFD) are extracted and then applied for training the proposed model. To empower the training phase of CRF-LSTM method, AOA is utilized to select optimal weight parameter coefficients of CRF-LSTM for training the model parameters. The proposed method is validated by statistical measurements and compared with the conventional methods such as Convolutional Neural Network-Particle Swarm Optimization (CNN-PSO) and Convolutional Neural Network (CNN) respectively.

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Section: Problem Definitionmentioning

confidence: 99%

Section: Exploitation Stagementioning

confidence: 99%

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Enhanced conditional random field‐long short‐term memory for name entity recognition in English texts

Bhumireddypalli¹,

Koppula²,

Koppula³

2023

Concurrency and Computation

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“…The primary goal of Clinical NER is to recognize and categorize clinical words in clinical data, such as symptoms, drugs, and therapy entity boundaries, and category labels are often anticipated simultaneously when approaching the topic as a sequence labeling issue [14]. Many studies have been undertaken to extract named elements from clinical literature through machine learning methodologies and deep neural network-based approaches [15][16]. The existing conventional NER models all use a neural network design that is devoid of handcrafted features, making them more exible to various applications, languages, and domains [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Clinical XLNet-based End-to-End Knowledge Discovery on Clinical Text Data using Natural Language Processing

Pagad

Chakrabarti

et al. 2022

Preprint

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A modern framework for assessing patient histories and conducting clinical research has been developed as the number of clinical narratives evolves. To discover the knowledge from such clinical narratives, clinical entity recognition and relation extraction tasks were performed subsequently in existing approaches, which resulted in error propagation. Therefore, a novel end-to-end clinical knowledge discovery strategy has been proposed in this paper. The clinical XLNet was used as a base model for handling the discrepancy issue. To predict the dependent clinical relation association, the multinomial Naïve Bayes probability function has been incorporated. In order to improve the performance of the proposed strategy, it takes into account entity pairs presented consecutively through the multi-head attention layer. Tests have been conducted using the N2C2 corpus, and the proposed methodology achieves a greater than 20% improvement in accuracy over existing neural network-based and transformer-based methods.

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Neural Text Classification and Stacked Heterogeneous Embeddings for Named Entity Recognition in SMM4H 2021

Cited by 2 publications

References 0 publications

Enhanced conditional random field‐long short‐term memory for name entity recognition in English texts

Enhanced conditional random field‐long short‐term memory for name entity recognition in English texts

Clinical XLNet-based End-to-End Knowledge Discovery on Clinical Text Data using Natural Language Processing

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