Answers.Body: <p>I think U are looking for PLSA
  for PLSA either U find out those topics(catogeries) with EM or NNMF
  Personally I recommend NNMF
  or u can use LDA which is Bayesian version of PLSA</p>
  <p>here is code for PLSA:
  <a href="https://github.com/Man-ash/Probabilistic-Latent-semantic-analysis" rel="nofollow noreferrer">https://github.com/Man-ash/Probabilistic-Latent-semantic-analysis</a>
  which use NNMF</p>
  <p>for EM method I code it by myself but i am not sure if it is right
  <a href="https://stackoverflow.com/questions/65783880/problem-about-m-step-in-probabilistic-latent-semantic-analysis-with-code">https://stackoverflow.com/questions/65783880/problem-about-m-step-in-probabilistic-latent-semantic-analysis-with-code</a></p>
  
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Answers.CreationDate: 2021-01-21T09:56:58.563Z
Answers.LastActivityDate: 2021-01-21T09:56:58.563Z
Answers.UserDisplayName: Linear Algebra fans
Answers.Body: <p>You can also pose your problem as co-reference resolution. Try Huggingface's <a href="https://github.com/huggingface/neuralcoref" rel="nofollow noreferrer">neuralcoref</a> library.</p>
  
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Answers.CreationDate: 2021-02-20T17:53:52.203Z
Answers.Text: I've noticed that you have provided many shorts answers. Sometimes, an answer doesn't have to be longer than 1-2 lines, but that's rarely the case. Note that I'm not saying that long answers are better. In fact, very long answers are also not good. I've seen so many answers that were bad only because they were so long and didn't focus on answering the question. So, please, before providing another answer, take a look at https://ai.stackexchange.com/help/how-to-answer. So, in this case, you could at least provide a link to what you're suggesting the OP to look into.
Answers.UserDisplayName: nbro
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Answers.CreationDate: 2021-02-20T17:50:31.117Z
Answers.LastActivityDate: 2021-02-20T18:20:11.943Z
Answers.LastEditDate: 2021-02-20T18:20:11.943Z
Answers.UserDisplayName: Abhishek Verma
Body: <p>lets say I have three texts:</p>
  <ol>
  <li>&quot;make a heading that says hello word&quot;</li>
  <li>&quot;make a heading of hello world&quot;</li>
  <li>&quot;create heading consist of hello world&quot;</li>
  </ol>
  <p>How can I fetch those groups of words using AI which is referring to heading i.e hello world in this case. Which AI frameworks or libraries can do that?</p>
  <p>in all examples heading is pointing to hello world (which i am referring as group of words). so basically i want those words which will be a part of heading or in other word there is a relationship between them. another example i can give is &quot;I am watching Breaking bad&quot; so there is a relationship between watching and breaking bad and i want to extract what are you watching.</p>
  <p>What's the best approach? Do I have to train a model for that or there are some other techniques that can get it done?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-01-13T13:49:14.573Z
Comments.Text: Please explain more clearly.  What does 'fetch those groups of words' mean?  You already have words.  Why do you want more words?  What is it you are hoping AI is going to do for you?
Comments.UserDisplayName: Brian O'Donnell
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-01-13T15:47:16.683Z
Comments.Text: in all examples heading is pointing to hello world (which i am referring as group of words). so basically i want those words which will be a part of heading or in other word there is a relationship between them. another example i can give is "I am watching Breaking bad" so there is a relationship between watching and breaking bad and i want to extract what are you watching. I hope I have made myself more clear. thanks for taking your time out.
Comments.UserDisplayName: ukasha sohail
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-01-13T16:01:07.013Z
Comments.Text: Hello @ukasha, could you update your question to include the details that you just provided - this may help you to get more answers
Comments.UserDisplayName: mark mark
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-01-14T01:47:25.920Z
Comments.Text: [Spacy](https://spacy.io/) is a machine learning library to (among others) analyze linguistic dependency and offers solutions for Named Entity Recognition (i.e. finding names or titles like "Breaking Bad" which refer to some entity). You can either use pretrained models or train your own models. So, this library might offer what you are looking for.
Comments.UserDisplayName: Daniel B.
Tags: <neural-networks><machine-learning><convolutional-neural-networks><natural-language-processing><natural-language-understanding>
Title: How to extract parameters from a text using AI/NLP
UserDisplayName: ukasha sohail
ThreadText: # How to extract parameters from a text using AI/NLP
  
  (Asked by: username_0 on 2021-01-13)
  
  lets say I have three texts:
  
  1. "make a heading that says hello word"
  2. "make a heading of hello world"
  3. "create heading consist of hello world"
  
  How can I fetch those groups of words using AI which is referring to heading i.e hello world in this case. Which AI frameworks or libraries can do that?
  
  in all examples heading is pointing to hello world (which i am referring as group of words). so basically i want those words which will be a part of heading or in other word there is a relationship between them. another example i can give is "I am watching Breaking bad" so there is a relationship between watching and breaking bad and i want to extract what are you watching.
  
  What's the best approach? Do I have to train a model for that or there are some other techniques that can get it done?
  
  **Tags:** neural-networks, machine-learning, convolutional-neural-networks, natural-language-processing, natural-language-understanding
  
  > **Comments:**
  > **username_1**: Please explain more clearly.  What does 'fetch those groups of words' mean?  You already have words.  Why do you want more words?  What is it you are hoping AI is going to do for you?  
  > **username_0**: in all examples heading is pointing to hello world (which i am referring as group of words). so basically i want those words which will be a part of heading or in other word there is a relationship between them. another example i can give is "I am watching Breaking bad" so there is a relationship between watching and breaking bad and i want to extract what are you watching. I hope I have made myself more clear. thanks for taking your time out.  
  > **username_2**: Hello @username_0, could you update your question to include the details that you just provided - this may help you to get more answers  
  > **username_3**: [Spacy](https://spacy.io/) is a machine learning library to (among others) analyze linguistic dependency and offers solutions for Named Entity Recognition (i.e. finding names or titles like "Breaking Bad" which refer to some entity). You can either use pretrained models or train your own models. So, this library might offer what you are looking for.  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_4 on 2021-01-21. Score: 0
  
  I think U are looking for PLSA
  for PLSA either U find out those topics(catogeries) with EM or NNMF
  Personally I recommend NNMF
  or u can use LDA which is Bayesian version of PLSA
  
  here is code for PLSA:
  <https://github.com/Man-ash/Probabilistic-Latent-semantic-analysis>
  which use NNMF
  
  for EM method I code it by myself but i am not sure if it is right
  <https://stackoverflow.com/questions/65783880/problem-about-m-step-in-probabilistic-latent-semantic-analysis-with-code>
  
  
  ---
  
  ### Answer by username_5 on 2021-02-20. Score: 0
  
  You can also pose your problem as co-reference resolution. Try Huggingface's [neuralcoref](https://github.com/huggingface/neuralcoref) library.
  
  > **Comments:**
  > **username_6**: I've noticed that you have provided many shorts answers. Sometimes, an answer doesn't have to be longer than 1-2 lines, but that's rarely the case. Note that I'm not saying that long answers are better. In fact, very long answers are also not good. I've seen so many answers that were bad only because they were so long and didn't focus on answering the question. So, please, before providing another answer, take a look at https://ai.stackexchange.com/help/how-to-answer. So, in this case, you could at least provide a link to what you're suggesting the OP to look into.  
  
  ---
  
  