Answers.Body: <p>There have been a few notable major attempts to build language-processing systems that use directed graphs (similar to, but more general than trees) out of word associations. Variations of your idea, called Symbolic AI, have been used in NLP projects of all sorts and sizes. It is still a common approach for modern chatbots for instance, that use databases of word types, synonyms and relationships.</p>
  <p>One well-known open-source project to do this is called <a href="https://github.com/commonsense/conceptnet5/wiki" rel="nofollow noreferrer">ConceptNet</a>. There are also initiatives such as <a href="https://www.wired.com/2016/03/doug-lenat-artificial-intelligence-common-sense-engine/" rel="nofollow noreferrer">CYC</a> which attempt to encode these rules into inference engines.</p>
  <p>A lot of recent focus has instead been on very large statistical models, e.g. <a href="https://openai.com/blog/openai-api/" rel="nofollow noreferrer">GPT-3</a>. These have performed far better at tasks such as text summarisation, text generation and translation than knowledge-encoding directly by engineers, despite the impressive sizes of CYC and ConceptNet.</p>
  <p>However, some problems remain with the large statistical models. They often produce grammatically sound but meaningless content that defies common sense. It is not clear how this will be addressed going forward, and in the short-to-medium term at least it is possible <a href="https://bdtechtalks.com/2019/06/05/mit-ibm-hybrid-ai/" rel="nofollow noreferrer">that hybrid systems that attempt to combine the best qualities of each</a> approach will be the most practical way to accurately process NLP tasks.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-03-24T16:14:02.170Z
Answers.LastActivityDate: 2022-03-24T22:16:51.823Z
Answers.LastEditDate: 2022-03-24T22:16:51.823Z
Answers.UserDisplayName: Neil Slater
Body: <p>I am novice to AI, but I would like to learn the general idea that a machine understands the concepts in a text document.</p>
  <p>I would like to ask wether there is an ontological tree of concepts, like a higher class of OBJECTS in which there belongs the FURINTURE, in which there belongs the CHAIR and the TABLE. Is such a thing in NLP? If yes, could someone provide me a way to find such relationships?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-03-25T10:22:24.110Z
Comments.Text: What were your findings before asking the question? This site is not a human search engine. You should have done a little bit of research on your own about your question before asking it, and share your findings with us. Please, next time, do a little bit of research and share with us the results before asking.
Comments.UserDisplayName: nbro
Tags: <natural-language-processing>
Title: ontological tree for the concept of a word (eg "chair")
UserDisplayName: yooyle
ThreadText: # ontological tree for the concept of a word (eg "chair")
  
  (Asked by: username_0 on 2022-03-24)
  
  I am novice to AI, but I would like to learn the general idea that a machine understands the concepts in a text document.
  
  I would like to ask wether there is an ontological tree of concepts, like a higher class of OBJECTS in which there belongs the FURINTURE, in which there belongs the CHAIR and the TABLE. Is such a thing in NLP? If yes, could someone provide me a way to find such relationships?
  
  **Tags:** natural-language-processing
  
  > **Comments:**
  > **username_1**: What were your findings before asking the question? This site is not a human search engine. You should have done a little bit of research on your own about your question before asking it, and share your findings with us. Please, next time, do a little bit of research and share with us the results before asking.  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2022-03-24. Score: 1 | Accepted answer
  
  There have been a few notable major attempts to build language-processing systems that use directed graphs (similar to, but more general than trees) out of word associations. Variations of your idea, called Symbolic AI, have been used in NLP projects of all sorts and sizes. It is still a common approach for modern chatbots for instance, that use databases of word types, synonyms and relationships.
  
  One well-known open-source project to do this is called [ConceptNet](https://github.com/commonsense/conceptnet5/wiki). There are also initiatives such as [CYC](https://www.wired.com/2016/03/doug-lenat-artificial-intelligence-common-sense-engine/) which attempt to encode these rules into inference engines.
  
  A lot of recent focus has instead been on very large statistical models, e.g. [GPT-3](https://openai.com/blog/openai-api/). These have performed far better at tasks such as text summarisation, text generation and translation than knowledge-encoding directly by engineers, despite the impressive sizes of CYC and ConceptNet.
  
  However, some problems remain with the large statistical models. They often produce grammatically sound but meaningless content that defies common sense. It is not clear how this will be addressed going forward, and in the short-to-medium term at least it is possible [that hybrid systems that attempt to combine the best qualities of each](https://bdtechtalks.com/2019/06/05/mit-ibm-hybrid-ai/) approach will be the most practical way to accurately process NLP tasks.
  
  
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