Affiliations: [a] Department of Computer Science and IT, University of Balochistan, Quetta, Pakistan
| [b] Laboratoire d’Informatique de Grenoble, Université Grenoble Alpes, Grenoble, France
Correspondence:
[*]
Corresponding author. Maheen Bakhtyar, Department of Computer Science and IT, University of Balochistan, Quetta, Pakistan. E-mail: [email protected].
Abstract: Unstructured text processing is the first step for several applications such as question answering systems, information retrieval, and recipe classification. In the field of recipe classification, number of frameworks have been proposed. However, it is still very tedious and time consuming to extract the food items from the unstructured text and then process for classification. In this research, an automatic food item detection from unstructured text is proposed based on semantic sense modeling. The candidate nouns are detected which can be food items and then the similarity of those nouns is computed with possible food categories. The candidate noun is treated as food item if the similarity is high. For similarity between possible food item and food category is computed by WordNet ontology. The proposed framework is evaluated on benchmark datasets and competitive performance have been achieved. The F-score on large dataset that contains around 20 K recipes is 0.89 which is improved from 0.56.
Keywords: Food named entity recognition, recipe text processing, NLP, semantic similarity, WordNet
