A Named Entity Based Approach to Model Recipes

• URL: http://arxiv.org/abs/2004.12184v1
• Date: Sat, 25 Apr 2020 16:37:26 GMT
• Title: A Named Entity Based Approach to Model Recipes
• Authors: Nirav Diwan, Devansh Batra and Ganesh Bagler
• Abstract summary: We propose a structure that can accurately represent the recipe as well as a pipeline to infer the best representation of the recipe in this uniform structure. Ingredients section in a recipe typically lists down the ingredients required and corresponding attributes such as quantity, temperature, and processing state. The instruction section lists down a series of events in which a cooking technique or process is applied upon these utensils and ingredients.
• Score: 9.18959130745234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional cooking recipes follow a structure which can be modelled very well if the rules and semantics of the different sections of the recipe text are analyzed and represented accurately. We propose a structure that can accurately represent the recipe as well as a pipeline to infer the best representation of the recipe in this uniform structure. The Ingredients section in a recipe typically lists down the ingredients required and corresponding attributes such as quantity, temperature, and processing state. This can be modelled by defining these attributes and their values. The physical entities which make up a recipe can be broadly classified into utensils, ingredients and their combinations that are related by cooking techniques. The instruction section lists down a series of events in which a cooking technique or process is applied upon these utensils and ingredients. We model these relationships in the form of tuples. Thus, using a combination of these methods we model cooking recipe in the dataset RecipeDB to show the efficacy of our method. This mined information model can have several applications which include translating recipes between languages, determining similarity between recipes, generation of novel recipes and estimation of the nutritional profile of recipes. For the purpose of recognition of ingredient attributes, we train the Named Entity Relationship (NER) models and analyze the inferences with the help of K-Means clustering. Our model presented with an F1 score of 0.95 across all datasets. We use a similar NER tagging model for labelling cooking techniques (F1 score = 0.88) and utensils (F1 score = 0.90) within the instructions section. Finally, we determine the temporal sequence of relationships between ingredients, utensils and cooking techniques for modeling the instruction steps.

Related papers

• Retrieval Augmented Recipe Generation [96.43285670458803]
  We propose a retrieval augmented large multimodal model for recipe generation. It retrieves recipes semantically related to the image from an existing datastore as a supplement. It calculates the consistency among generated recipe candidates, which use different retrieval recipes as context for generation.
  arXiv  Detail & Related papers  (2024-11-13T15:58:50Z)
• KitchenScale: Learning to predict ingredient quantities from recipe contexts [13.001618172288198]
  KitchenScale is a model that predicts a target ingredient's quantity and measurement unit given its recipe context. We formulate an ingredient quantity prediction task that consists of three sub-tasks which are ingredient measurement type classification, unit classification, and quantity regression task. Experiments with our newly constructed dataset and recommendation examples demonstrate KitchenScale's understanding of various recipe contexts.
  arXiv  Detail & Related papers  (2023-04-21T04:28:16Z)
• Assorted, Archetypal and Annotated Two Million (3A2M) Cooking Recipes Dataset based on Active Learning [2.40907745415345]
  We present a novel dataset of two million culinary recipes labeled in respective categories. To construct the dataset, we collect the recipes from the RecipeNLG dataset. There are more than two million recipes in our dataset, each of which is categorized and has a confidence score linked with it.
  arXiv  Detail & Related papers  (2023-03-27T07:53:18Z)
• Counterfactual Recipe Generation: Exploring Compositional Generalization in a Realistic Scenario [60.20197771545983]
  We design the counterfactual recipe generation task, which asks models to modify a base recipe according to the change of an ingredient. We collect a large-scale recipe dataset in Chinese for models to learn culinary knowledge. Results show that existing models have difficulties in modifying the ingredients while preserving the original text style, and often miss actions that need to be adjusted.
  arXiv  Detail & Related papers  (2022-10-20T17:21:46Z)
• Assistive Recipe Editing through Critiquing [34.1050269670062]
  RecipeCrit is a hierarchical denoising auto-encoder that edits recipes given ingredient-level critiques. Our work's main innovation is our unsupervised critiquing module that allows users to edit recipes by interacting with the predicted ingredients.
  arXiv  Detail & Related papers  (2022-05-05T05:52:27Z)
• Learning Structural Representations for Recipe Generation and Food Retrieval [101.97397967958722]
  We propose a novel framework of Structure-aware Generation Network (SGN) to tackle the food recipe generation task. Our proposed model can produce high-quality and coherent recipes, and achieve the state-of-the-art performance on the benchmark Recipe1M dataset.
  arXiv  Detail & Related papers  (2021-10-04T06:36:31Z)
• Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning [17.42688184238741]
  Cross-modal recipe retrieval has recently gained substantial attention due to the importance of food in people's lives. We propose a simplified end-to-end model based on well established and high performing encoders for text and images. Our proposed method achieves state-of-the-art performance in the cross-modal recipe retrieval task on the Recipe1M dataset.
  arXiv  Detail & Related papers  (2021-03-24T10:17:09Z)
• Structure-Aware Generation Network for Recipe Generation from Images [142.047662926209]
  We investigate an open research task of generating cooking instructions based on only food images and ingredients. Target recipes are long-length paragraphs and do not have annotations on structure information. We propose a novel framework of Structure-aware Generation Network (SGN) to tackle the food recipe generation task.
  arXiv  Detail & Related papers  (2020-09-02T10:54:25Z)
• Multi-modal Cooking Workflow Construction for Food Recipes [147.4435186953995]
  We build MM-ReS, the first large-scale dataset for cooking workflow construction. We propose a neural encoder-decoder model that utilizes both visual and textual information to construct the cooking workflow.
  arXiv  Detail & Related papers  (2020-08-20T18:31:25Z)
• Decomposing Generation Networks with Structure Prediction for Recipe Generation [142.047662926209]
  We propose a novel framework: Decomposing Generation Networks (DGN) with structure prediction. Specifically, we split each cooking instruction into several phases, and assign different sub-generators to each phase. Our approach includes two novel ideas: (i) learning the recipe structures with the global structure prediction component and (ii) producing recipe phases in the sub-generator output component based on the predicted structure.
  arXiv  Detail & Related papers  (2020-07-27T08:47:50Z)
• Classification of Cuisines from Sequentially Structured Recipes [8.696042114987966]
  classification of cuisines based on their culinary features is an outstanding problem. We have implemented a range of classification techniques by accounting for this information on the RecipeDB dataset. The state-of-the-art RoBERTa model presented the highest accuracy of 73.30% among a range of classification models.
  arXiv  Detail & Related papers  (2020-04-26T05:40:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.