## 1 Introduction  
The aim of named-entity recognition (NER) is to detect chunks in a sentence representing an entity and assign a class to that entity. Named-entity recognition (NER) is one of the first and most important steps in Information Extraction pipelines that is to identify mentions of entities (persons, locations, organizations, menus, etc.) within unstructured text [1]. Although NER tasks have been effectively applied to various application domains and the general domain, several application domains remain not well discovered. The growing numbers of emerging food dishes with their diverse forms make recognizing food entities on social media platforms more challenging. Another challenge posed by user-generated content is its unique characteristics and use of informal language, which is typically short context, noisy, sparse and ambiguous contents [1]. In deep learning, NER is considered a sequence labeling task where the neural models usually contain three components: word embedding layer, context encoder layer, and decoder layer [2]. Recurrent Neural Networks (RNNs) models, specifically, Bidirectional long short-term memory networks (Bi-LSTM) [3], due to their sequential characteristics and tremendous capability to learn the contextual representation of words, and Conditional Random Fields (CRF) [4], due to better sentence-level prediction are widely adapted for context encoder and decoder layers for sequence labeling tasks [5; 6; 7; 8]. For word embedding, pre-trained model-based word embeddings, such as word2 vec [9] and GloVe [10], and other contextualized word embeddings, such as ELMo [11] and BERT [12], have been utilized. As a state-of-the-art pre-trained language model, BERT (Bidirectional Encoder Representations from Transformers) has achieved state-of-the-art accuracy in various NLP tasks, including text classification [13], machine translation [14] and named-entity recognition [15], etc. BERT is based on a multi-layer bidirectional transformer [16] architecture. It is pre-trained on a large amount of unlabeled data for masked word prediction and next sentence prediction tasks [12], which can then be fine-tuned on a labeled dataset for a specific downstream task. Developing BERT pre-trained model for a new domain from scratch is quite expensive since even the BERT-base model has 12-layer with roughly 110 million parameters. To train such a dense network requires a huge amount of dataset. It is extremely difficult for the majority of application domains, including the food domain, to make a dataset with that many documents. Furthermore, training such a large dataset requires a huge amount of computing resources. A logical notion is to further pre-train the original off-the-shelf BERT with a limited target-domain dataset. When further pre-trained on domain-specific corpora, the BERT pre-trained model can improve performance in domain-specific tasks while maintaining good performance in general domain tasks [17; 18]. The idea is termed as domain adaptation or domain transfer technique and has been applied in various NLP applications [13; 19; 20]. Post-training approaches for domain-adaptation are highly effective for the tasks where the training dataset is limited [17]. The main challenge in domain-specific NER is the lack of an annotated corpus. In the case of food menu domain which has a complex set of multilingual global and regional cuisines names, finding food menu entities is a challenging task requiring domain-specific pre-trained models to learn the actual context of the text. To address the limited number of hand-crafted training examples in the application domain, we first apply domain adaption to incorporate domain-specific weights in the vector space by further pre-training off-the-shelf BERT-base model (as shown in Figure 1) on target-domain dataset (YELP reviews dataset) to learn the better representation of target-domain entities (food menu entities) on user-generated text (as shown in Figure 2).