## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Ensemble Scheme** & **Classifiers** & **Dataset** &
\begin{tabular}{c} **Performance** \\ **Metrics** \\ \end{tabular} \\ \hline
[9] & Voting & MLP, ABM1, J48 & Reuters corpus & Recall, Precision, Error rate, MCC, F1 score \\ \hline
[10] & Weighted Voting & ME, CRF, SVM &
\begin{tabular}{c} Bengali News corpus, \\ NERSSEAL, CoNLL-2003 \\ \end{tabular} & F1 score \\ \hline
[11] & Concatenation & Neural Networks &
\begin{tabular}{c} OKE2016, AIDA/CoNLL, \\ NexGenTV corpus \\ \end{tabular} & Recall, Precision, F1 score \\ \hline
[12] & \
\begin{tabular}{c} Arbitration Rules, \\ Stacked Generalization, \\ Cascade Generalization \\ \end{tabular} &
\begin{tabular}{c} Generalized Winnow, ME, \\ SVM, CRF \\ \end{tabular} & GENIA, JNLPBA & Recall, Precision, F1 score \\ \hline
[13] & Voting & \
\begin{tabular}{c} 5 NER systems: Stanford NER, \\ NER-Tagger, Edinburgh \\ Geoparser, spaCy NER, \\ Polyglot \\ \end{tabular} &
\begin{tabular}{c} The Marry Hamilton \\ Papers, The Samuel Hartlib \\ Papers \\ \end{tabular} & Recall, Precision, F1 score \\ \hline
[14] & Majority Voting &
\begin{tabular}{c} BERT, CNN, CamemBERT, \\ CamemBERT-bio, XL-Net, \\ RoBERTa, BioBERT, Bio + \\ ClinicalBERT, PubMedBERT, \\ BioMed RoBERTa \\ \end{tabular} & ChEMU 2020, DEFT 2020, \\ \hline
[15] & Majority Voting, Stacking & SVM, CRF, ME & i2b2 2010 corpus & Recall, Precision, F1 score \\ \hline
[16] & Majority Voting &
\begin{tabular}{c} BERT-base-cased, \\ BERT-based-uncased, CNN \\ \end{tabular} & ChEMU NER Task & Recall, Precision, F1 score \\ \hline
[17] & \
\begin{tabular}{c} Plurality Voting, \\ Weighted Voting \\ \end{tabular} & HMM, CRF, MEM, BiLSTM &
\begin{tabular}{c} Private (Authors private \\ data) \\ \end{tabular} & F1 score \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of previous studies on NER using ensemble method. methods help reduce the variance of prediction errors, the final framework is expected to be more robust than the individual models that comprise the ensemble [22]. Therefore, we expect that an RNE that leverages the advantages of deep RNNs and ensemble learning can provide a useful framework for NER in identifying food ingredient entities. We trained three independent deep learning models (RNN, GRU, and LSTM) on the same dataset and fused their prediction with the ensemble concatenation [11] approach for a final prediction. Our main contributions in this paper are summarized as follows:  
* Proposing a novel approach to extract food-related entities using an ensemble method with recurrent networks models such as RNN, GRU, and LSTM. To the best of the authors' knowledge, this is the first work to explore a model that benefits from a deep learning-based ensemble method applied to a food-related NER task. * Preparing a carefully annotated dataset for food ingredient entities, namely the FINER dataset. * Achieving better results on the proposed NER task approach compared to single models for food-related NER. In this paper, we present a robust strategy for extracting food ingredient information from recipe text. The rest of the chapter is organized as follows: Section 2 presents an overview of the related works; Section 3 defines the proposed method by constructing the food ingredient dataset, deep learning classifiers, and finally our proposed method RNE; Section 4 analyzes and discusses the results of the proposed method; and Section 5 concludes the study.