Answers.Body: <p>Since you want a shortcut use the <a href="https://spoonacular.com/food-api/docs/detect-foods-in-text" rel="nofollow noreferrer">spoonacular API</a>.  Below is a test with your words.  You can see it had trouble with 'Coca' and 'veg'.</p>
  <p><a href="https://i.sstatic.net/ZzoTg.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/ZzoTg.jpg" alt="enter image description here" /></a></p>
  <p>What you need is 'named-entity recognition' for food.  This is not a new thing but clearly not a solved problem.  The <a href="https://github.com/vabraham/foodie_favorites" rel="nofollow noreferrer">Foodie Favorites</a> repository attempts to solve the problem from scratch.</p>
  <p>If want to do some research and dig deeper see <a href="https://academic.oup.com/database/article/doi/10.1093/database/baz121/5611291" rel="nofollow noreferrer">FoodBase corpus: a new resource of annotated food entities</a>.  From the abstract:</p>
  <blockquote>
  <p>It consists of 12,844 food entity annotations describing 2105 unique food entities. Additionally, we provided a weakly annotated corpus on an additional 21,790 recipes. It consists of 274,053 food entity annotations, 13,079 of which are unique.</p>
  </blockquote>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-01-02T01:45:09.053Z
Answers.LastActivityDate: 2021-01-02T01:45:09.053Z
Answers.UserDisplayName: Brian O'Donnell
Body: <p>I am new to NLP and AI in general. I am just expecting springboard information so that I can skip all the introduction to NLP websites.  I have just started studying NLP and want to know how to go about solving this problem. I am creating a chatbot that will take voice input from customers ordering food at restaurants. The customer input I am expecting as;</p>
  <blockquote>
  <p>I want to order Chicken Biryani</p>
  </blockquote>
  <blockquote>
  <p>Can I have a Veg Pizza, please</p>
  </blockquote>
  <blockquote>
  <p>Coca-cola  etc</p>
  </blockquote>
  <p>I want to write an algorithm that can separate the name of the food item from the user input and compare it with the list of food items in my menu card and come up with the right item.</p>
  <p>I am new to NLP, I am studying it online for this particular project, I can do the required coding, I just need help with the overall algo or sort of flow chart. It will save my time tremendously. Thanks.</p>
  
Tags: <machine-learning><natural-language-processing><algorithm>
Title: How to design a NLP algorithm to find a food item in menu card list?
UserDisplayName: The White Cloud
ThreadText: # How to design a NLP algorithm to find a food item in menu card list?
  
  (Asked by: username_0 on 2021-01-01)
  
  I am new to NLP and AI in general. I am just expecting springboard information so that I can skip all the introduction to NLP websites. I have just started studying NLP and want to know how to go about solving this problem. I am creating a chatbot that will take voice input from customers ordering food at restaurants. The customer input I am expecting as;
  
  > I want to order Chicken Biryani
  
  > Can I have a Veg Pizza, please
  
  > Coca-cola etc
  
  I want to write an algorithm that can separate the name of the food item from the user input and compare it with the list of food items in my menu card and come up with the right item.
  
  I am new to NLP, I am studying it online for this particular project, I can do the required coding, I just need help with the overall algo or sort of flow chart. It will save my time tremendously. Thanks.
  
  **Tags:** machine-learning, natural-language-processing, algorithm
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-01-02. Score: 2 | Accepted answer
  
  Since you want a shortcut use the [spoonacular API](https://spoonacular.com/food-api/docs/detect-foods-in-text). Below is a test with your words. You can see it had trouble with 'Coca' and 'veg'.
  
  [![enter image description here](https://i.sstatic.net/ZzoTg.jpg)](https://i.sstatic.net/ZzoTg.jpg)
  
  What you need is 'named-entity recognition' for food. This is not a new thing but clearly not a solved problem. The [Foodie Favorites](https://github.com/vabraham/foodie_favorites) repository attempts to solve the problem from scratch.
  
  If want to do some research and dig deeper see [FoodBase corpus: a new resource of annotated food entities](https://academic.oup.com/database/article/doi/10.1093/database/baz121/5611291). From the abstract:
  
  > It consists of 12,844 food entity annotations describing 2105 unique food entities. Additionally, we provided a weakly annotated corpus on an additional 21,790 recipes. It consists of 274,053 food entity annotations, 13,079 of which are unique.
  
  
  ---
  
  