## 1 Introduction  
In the not-so-distant past, personal collections of recipes were kept in binders, notes, and magazine clippings. Nowadays, the internet and digitization are changing these practices. People are starting to post their favorite recipes on recipe sharing sites. These recipe sharing sites introduce people worldwide to new dishes suitable for their lifestyle and well-being. Thousands of recipes are available online for individuals who enjoy cooking at home or planning their menus. In addition, this food-related information is useful for developing health and health care-related systems, such as personalized recommendation systems for individual diets [1, 2], calculating nutrient levels in foods to prevent malnutrition [3], and identifying alternative ingredients for individuals with food allergies [4, 5]. However, the information from online recipes is often given in the format of unstructured text. One of the most basic tools to extract such information into a well-structured format is the technique called named-entity recognition (NER). NER enables us to recognize and extract essential pieces of information from text, such as names, locations, organizations, and so on [5, 6]. NER can also help various enterprises and organizations discover insights from massive unstructured datasets. Furthermore, it can be useful for analyzing relations that exist between these entities. In the case of food and meal processing terms, NER can help find some hidden relations between food entities and disease entities for food allergies studies. Numerous studies have been conducted on NER applications in various fields [7; 8]. Most of this research employs single classifiers such as CRF, HMM, LSTM, Bi-LSTM, BERT, and their variations, and tried to combine them in an end-to-end manner like Bi-LSTM-CRF. In doing so, they failed to benefit from the diversity of existing NER algorithms. On the other hand, it is a well-known fact that algorithms with different strengths and weaknesses can be coupled in various ways to build a model that surpasses the best individual algorithm. This type of learning is referred to as the ensemble method. As shown in Table 1, the majority of ensemble methods for NER employ voting techniques, whereas only a few employ stacking and concatenation techniques. However, none of these studies has yet incorporated it as a solution to the issue of extracting food-related entities. Therefore, we propose the recurrent network-based ensemble (RNE) method, an ensemble approach involving deep recurrent neural networks (RNNs) to extract food entities from unstructured textual data. The concept behind utilizing RNNs for NER is to make use of sequential information. NER is a classification problem at the token level. Thus, the ability to understand sentence context from beginning to end is needed for more accurate predictions. Recurrent neural networks (RNNs), along with their variants such as gated recurrent unit (GRU) and long-short-term memory (LSTM), have shown remarkable results in sequential data modelling [8; 18]. For example, NER architectures that integrated and employed RNNs as a feature extractor for word context or word characters have shown to be powerful NER systems that require little domain-specific knowledge or resources [18; 19; 20]. Furthermore, a bidirectional RNN has been proven to efficiently utilize future information (via the backward state) as well as past information (via the forward state) for a certain period of time [21]. On the other hand, ensemble learning is a method to leverage consensus in prediction by combining relevant aspects of different models. Since the ensemble