# A Survey of the Applications of Text Mining for the Food Domain
## 3 Text Mining Overview
### Named Entity Recognition  
Named entity recognition (NER) is a fundamental task in the field of NLP. Its objective is to identify entities in a text with special significance, such as personal names, geographic locations, and organization names [47]. Entities can be broadly categorized into general and domain-specific types. The key to NER lies in developing models capable of eliminating text ambiguities, accurately delineating entity boundaries, and assigning appropriate categories. Methods for achieving this goal include rules-based approaches, statistical techniques, and deep learning. NER plays a pivotal role in supporting tasks like information extraction, question-answering systems, and relationship extraction. Table 4 summarizes common NER methods along with their advantages and disadvantages, accompanied by relevant literature utilizing these approaches. The application of NER has greatly expanded in the field of food, encompassing the identification of entities such as ingredient names, brand names, and product specifications in food menus, packaging, and review texts. This provides possibilities for various downstream applications [51]. For example, [PERSON] et al. [48] employed a rules-based NER method named drNER to extract knowledge on evidence-based dietary advice from unstructured text. In NER tasks within the food domain, it is necessary to identify domain-specific terms such as ingredient names, dishes, and food technologies. Additionally, NER models must handle specific language conventions, such as ingredient omissions and pronoun usage in menus. Establishing an NER system for the food domain requires the construction of domain-specific texts to create annotated datasets and the incorporation of contextual features and vocabulary constraints in the text to enhance the model's recognition capabilities. For instance, [PERSON] et al. [49] proposed a rules-based NER method called FoodIE for extracting entities related to food from unstructured text data. This method integrates rules from computational linguistics and semantic information to describe features of food entities, addressing shortcomings in information-extraction methods specifically designed for food concepts in NLP. In further research, FoodIE and a gold standard corpus named FoodBase were utilized for an in-depth exploration of the food domain [50]. Additionally, researchers introduced a scientific food NER and Named Entity Linking (NEL) model called SciFoodNER, which fine-tunes a Transformer model and leverages a scientific abstract corpus annotated with food entities [52]. The evolution of NER technology has traversed from initial rules-based methodologies to statistical learning techniques and, notably, to the era of deep learning. Within deep learning, various neural network models, including CNN, RNN, LSTM, and BERT, have substantially enhanced NER's performance [47]. For instance, [PERSON] et al. [53] compared the performance of seven text mining models for food and dietary ingredient NER, revealing differences between classical machine learning models and the latest deep language models. Contemporary NER systems can be trained end-to-end, eliminating the need for manual feature engineering, thanks to pre-trained language models. Additionally,