A Hybrid Approach for Food Name Recognition in Restaurant Reviews

Food Computing is an emerging research field that leverages Natural Language Processing (NLP) techniques to extract valuable insights from textual data. A key task within NLP is Named Entity Recognition (NER), which involves identifying and categorizing words or phrases into predefined categories. Current, NER methods are limited in their capacity to recognize novel entity types, such as food names. Enhancing their capabilities to encompass new entities necessitates supervised training, that needs substantial labeled dataset. Labeling such datasets is time-intensive and challenging, particularly for novel entities like foods, that lack standardized definitions across various applications. Furthermore, existing state-of-the-art transformer-based techniques are not suitable for lightweight applications due to their large size and computational complexity. In this study, we present a neuro-heuristic based approach for food name recognition, specifically targeting food names or recipe names. To mitigate the need for extensive labeling, we adopt a template-based approach to prepare a dataset with labeled food entities. Our system achieves an impressive F1 accuracy of 0.97, on the dataset prepared by using multiple publicly available resources, including the Branded Food Dataset and NPR Dataset.