Answers.Body: <p>I think you can solve this problem with models trained for Named Entity Recognition. In that case, your entities are labels. To do this you can use <a href="https://spacy.io/api/entityrecognizer" rel="nofollow noreferrer">spacy</a> to train a NER model or more easily you can fine-tune a <a href="https://huggingface.co/docs/transformers/model_doc/distilbert" rel="nofollow noreferrer">Distil-BERT</a> for your task.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-12-04T15:11:49.847Z
Answers.LastActivityDate: 2021-12-04T15:11:49.847Z
Answers.UserDisplayName: Seyed Moein Ayyoubzadeh
Body: <p>Looking for suggestions on how to define the following NLP problem and different ways in which it can be modeled to leverage machine learning. I believe there are multiple ways to model this problem.  Deep-learning-based suggestions also work as there is a good amount of data is available for training.</p>
  <p>Will evaluate different approaches for the given dataset. Please share relevant papers, blogs, or GitHub repos. Thanks!</p>
  <p><strong>Input</strong>: Given a sentence S having words W1 to W10.</p>
  <p>S = W1 W2 W3 W4 W5 W6 W7 W8 W9 W10</p>
  <p>The sentence has some syntactic and semantic patterns, but it is not exactly freely written natural language but it's in English. These are words, can be punctuation</p>
  <p><strong>Output</strong>: should be something like this.</p>
  <p>Label1 - W4</p>
  <p>Label2 - W3</p>
  <p>Label3 - [W2 W1] continuous // semantically related. Means words [W2 W1] in-order are assigned a Label3. <em>Also okay with solutions that don't output in-order.</em></p>
  <p>Label4 - [W6 W8]</p>
  <p>Label5- W10</p>
  <p>Noise- W7, W9. Means words W7 and W9 independently are assigned a
  Label3.</p>
  <p>Label7- W5</p>
  <p>Need to solve the problem. Looking for research/thoughts on how this problem can be defined in different ways to exploit different patterns in the structure of sentences. Looking for similar tasks which are already defined in NLP such as token labeling, parsing which can be used.</p>
  <p>Would be really helpful to get the suggestions to the latest research on solving/defining this problem.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-18T14:16:16.863Z
Comments.Text: Hello. Could you please put your **specific** question in the title?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-12-05T20:54:25.153Z
Comments.Text: What about parsing French sentences? See also [RefPerSys](http://refpersys.org/)
Comments.UserDisplayName: Basile Starynkevitch
Tags: <neural-networks><machine-learning><natural-language-processing><recurrent-neural-networks>
Title: NLP problem Phrase/Token labeling
UserDisplayName: user2358715
ThreadText: # NLP problem Phrase/Token labeling
  
  (Asked by: username_0 on 2021-09-18)
  
  Looking for suggestions on how to define the following NLP problem and different ways in which it can be modeled to leverage machine learning. I believe there are multiple ways to model this problem. Deep-learning-based suggestions also work as there is a good amount of data is available for training.
  
  Will evaluate different approaches for the given dataset. Please share relevant papers, blogs, or GitHub repos. Thanks!
  
  **Input**: Given a sentence S having words W1 to W10.
  
  S = W1 W2 W3 W4 W5 W6 W7 W8 W9 W10
  
  The sentence has some syntactic and semantic patterns, but it is not exactly freely written natural language but it's in English. These are words, can be punctuation
  
  **Output**: should be something like this.
  
  Label1 - W4
  
  Label2 - W3
  
  Label3 - [W2 W1] continuous // semantically related. Means words [W2 W1] in-order are assigned a Label3. *Also okay with solutions that don't output in-order.*
  
  Label4 - [W6 W8]
  
  Label5- W10
  
  Noise- W7, W9. Means words W7 and W9 independently are assigned a
  Label3.
  
  Label7- W5
  
  Need to solve the problem. Looking for research/thoughts on how this problem can be defined in different ways to exploit different patterns in the structure of sentences. Looking for similar tasks which are already defined in NLP such as token labeling, parsing which can be used.
  
  Would be really helpful to get the suggestions to the latest research on solving/defining this problem.
  
  **Tags:** neural-networks, machine-learning, natural-language-processing, recurrent-neural-networks
  
  > **Comments:**
  > **username_1**: Hello. Could you please put your **specific** question in the title?  
  > **username_2**: What about parsing French sentences? See also [RefPerSys](http://refpersys.org/)  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_3 on 2021-12-04. Score: 0
  
  I think you can solve this problem with models trained for Named Entity Recognition. In that case, your entities are labels. To do this you can use [spacy](https://spacy.io/api/entityrecognizer) to train a NER model or more easily you can fine-tune a [Distil-BERT](https://huggingface.co/docs/transformers/model_doc/distilbert) for your task.
  
  
  ---
  
  