Recurrent Neural Network-Based Model for Named Entity Recognition with Improved Word Embeddings

Goyal, Archana; Gupta, Vishal; Kumar, Manish

doi:10.1080/03772063.2021.2006805

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…They have utilized to forecast the patterns by using CRFs and BiLSTM models. [93] proposed an innovative strategy for creating a bilingual NER system as the IWE is used by the proposed approach to create the deep learning-based model. The authors applied Bi-LSTM [85] based on DL which contains POS and CNN embedding character and overall accuracy has been calculated by using F1-score, recall and precision.…”

Section: (C) Deep Learning Approachmentioning

confidence: 99%

“…They deleted the IOB formatting from the IJCNLP 200 NER Corpus in accordance with previous work on the corpus. It also has mainly three dataset such as IJCNLP 200 NER [10], IJCNLP-2008 [21], [78] and IJCNLP-08 [93].…”

Section: Datasetmentioning

confidence: 99%

“…These tools for evaluating the criteria manually and automatically encompasses the common datasets. The most popular datasets used in NER for Indian Language and their evaluation are presented in this stateof-the-art review [25], [68] [88], [93]. Their respective details are listed in Table 7.…”

Section: Datasetmentioning

confidence: 99%

See 2 more Smart Citations

State-of-art approach for Indian Language based on NER: Comprehensive Review

Pandey,

Nathani

2024

Preprint

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Named Entity Recognition (NER) is a fundamental task of natural language processing (NLP) that focuses on the identification and classification of named entities such as name of individual persons, location, organization and dates within the text. NER plays a pivotal role in various NLP applications, including information extraction, question answering, text summarization and sentiment analysis. Natural language processing's (NLPs) fundamental issue is named entity recognition (NER). While extensive research has been conducted on NER for English and Hindi, the complexities of Indian languages present unique challenges that require customized solutions. Working with NER for Indian languages is a difficult endeavor with limited resources available. This article provides a comprehensive review of NER approaches tailored for Indian languages. Indian languages pose unique challenges to NER due to their rich morphological and syntactic variations, script diversity and limited annotated data availability. This paper reviews the various techniques and methodologies employed in NER for Indian languages, including rule-based, machine learning and deep learning approaches. It analyzes the strengths and limitations of each approach. Additionally, this article examines the recent advancements in transfer learning and multilingual models, showcasing their potential in improving NER performance across Indian languages. This paper aims to guide researchers and practitioners in the development of NER systems for Indian languages and foster further advancements in this field. This article also provides a comprehensive review of the diverse approaches employed for NER in Indian languages, highlighting the strength and limitations as well.

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Section: (C) Deep Learning Approachmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

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State-of-art approach for Indian Language based on NER: Comprehensive Review

Pandey,

Nathani

2024

Preprint

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“…Thirdly, deep learning models consisting of many stacked neural networks are widely used in NER, such as RNN [25,26], Long Short-Time Memory (LSTM) [27], Bidirectional LSTM [28,29], BiLSTM-CRF [30,31], BERT-BiLSTM-CRF [6,32], ALBERT-BiLSTM-CRF [8], and attention mechanisms [33], which have replaced statistical machine learning techniques. This method can handle high-dimensional data with excellent accuracy.…”

Section: Named Entity Recognition Techniquesmentioning

confidence: 99%

Chinese Named Entity Recognition in Football Based on ALBERT-BiLSTM Model

An,

Pan,

Liu

et al. 2023

Applied Sciences

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Football is one of the most popular sports in the world, arousing a wide range of research topics related to its off- and on-the-pitch performance. The extraction of football entities from football news helps to construct sports frameworks, integrate sports resources, and timely capture the dynamics of the sports through visual text mining results, including the connections among football players, football clubs, and football competitions, and it is of great convenience to observe and analyze the developmental tendencies of football. Therefore, in this paper, we constructed a 1000,000-word Chinese corpus in the field of football and proposed a BiLSTM-based model for named entity recognition. The ALBERT-BiLSTM combination model of deep learning is used for entity extraction of football textual data. Based on the BiLSTM model, we introduced ALBERT as a pre-training model to extract character and enhance the generalization ability of word embedding vectors. We then compared the results of two different annotation schemes, BIO and BIOE, and two deep learning models, ALBERT-BiLSTM-CRF and ALBERT BiLSTM. It was verified that the BIOE tagging was superior than BIO, and the ALBERT-BiLSTM model was more suitable for football datasets. The precision, recall, and F-Score of the model were 85.4%, 83.47%, and 84.37%, correspondingly.

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“…Following this, several additional studies also investigated RNNs for sequence-labeling problems. The authors of [20,[31][32][33] used LSTM, whereas the authors of [34][35][36][37] used GRU in completing their NER tasks. In addition, the RNN-based model, the authors of [38] employ RNNs for nested NER problems and the authors of [39] use RNNs for NER in Chinese electronic medical records.…”

Section: Recurrent Network Model For Nermentioning

confidence: 99%

Enhancing Food Ingredient Named-Entity Recognition with Recurrent Network-Based Ensemble (RNE) Model

Komariah

Sin

2022

Applied Sciences

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Food recipe sharing sites are becoming increasingly popular among people who want to learn how to cook or plan their menu. Through online food recipes, individuals can select ingredients that suit their lifestyle and health condition. Information from online food recipes is useful in developing food-related systems such as recommendations and health care systems. However, the information from online recipes is often unstructured. One way of extracting such information into a well-structured format is the technique called named-entity recognition (NER), which is the process of identifying keywords and phrases in the text and classifying them into a set of predetermined categories, such as location, persons, time, and others. We present a food ingredient named-entity recognition model called RNE (recurrent network-based ensemble methods) to extract the entities from the online recipe. RNE is an ensemble-learning framework using recurrent network models such as RNN, GRU, and LSTM. These models are trained independently on the same dataset and combined to produce better predictions in extracting food entities such as ingredient names, products, units, quantities, and states for each ingredient in a recipe. The experimental findings demonstrate that the proposed model achieves predictions with an F1 score of 96.09% and outperforms all individual models by 0.2% to 0.5% in percentage points. This result indicates that RNE can extract information from food recipes better than a single model. In addition, this information extracted by RNE can be used to support various information systems related to food.

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Recurrent Neural Network-Based Model for Named Entity Recognition with Improved Word Embeddings

Cited by 6 publications

References 13 publications

State-of-art approach for Indian Language based on NER: Comprehensive Review

State-of-art approach for Indian Language based on NER: Comprehensive Review

Chinese Named Entity Recognition in Football Based on ALBERT-BiLSTM Model

Enhancing Food Ingredient Named-Entity Recognition with Recurrent Network-Based Ensemble (RNE) Model

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