Answers.Body: <p>This is a categorization problem, not unlike a spam filter. Instead of flagging an email as spam/not-spam, you are flagging whether it has one of the action categories that you have described.</p>
  
  <p>You'll need to start by assembling a training corpus of example email messages and labeling each example to identify which (maybe multiple) of your categories, if any, are actually present in that email. </p>
  
  <p>Next, pre-process that data to extract features for each message. Examples of typical features include word (or n-gram) counts/frequencies (bag of words). As a shortcut, you might to include as a feature a boolean indicating the presence or absence of a particular word or phrase that you suspect will be predictive of one or more categories. Techniques such as stemming can help reduce the number of words/n-grams being used (often increasing accuracy). </p>
  
  <p>Once you have a dataset that consists of features and labels for each training email (possibly breaking this set up into subsets for training, cross-validation, and testing), you'll want to apply a supervised classification algorithm. You might start with linear classifiers such as logistic regression or SVMs, and if you're unsatisfied with the resulting accuracy then you could advance to neural techniques. </p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-03-18T11:17:14.693Z
Answers.Text: "straightforward" ? too optimistic
Answers.UserDisplayName: pasaba por aqui
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-03-19T02:57:06.757Z
Answers.Text: Optimistic perhaps, but all is relative. :-) I'll try to expand a bit more on high level steps. As a note to beginners who want to dig deeper into implementation, Andrew Ng's Coursera course might be a good starting point.
Answers.UserDisplayName: G__
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-03-18T06:10:15.717Z
Answers.LastActivityDate: 2018-03-19T03:10:09.933Z
Answers.LastEditDate: 2018-03-19T03:10:09.933Z
Answers.UserDisplayName: G__
Body: <p>I am trying to build a model which would take an email message (in English, extracted subject, and body of the email) and identify if it has a question, request or a proposal. Basically, I would like to see the mails that I've not replied but needs a reply. The model can be used as a "filter" in an email client.</p>
  
  <p>What is the best way to go about it?</p>
  
  <p>Related Work:</p>
  
  <p><a href="https://github.com/ParakweetLabs/EmailIntentDataSet/wiki" rel="nofollow noreferrer">Parakweet Lab's Email Intent Data Set</a></p>
  
  <p><a href="https://www.cs.cmu.edu/~tom/EMNLP2004_final.pdf" rel="nofollow noreferrer">Learning to Classify Email into "Speech Acts"</a></p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-07-18T16:42:54.933Z
Comments.Text: When you're trying to implement some project or working on a project model,you need to give a snippet of what you're working. In order to get effective feedback from the community.For your information check through the [guide lines](https://ai.stackexchange.com/help)
Comments.UserDisplayName: quintumnia
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-07-19T07:36:07.313Z
Comments.Text: @quintumnia edited and gave a bit more detail.
Comments.UserDisplayName: convergence
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-09-07T15:41:34.790Z
Comments.Text: Am working on a similar use case.
  Could you implement? If yes, please share the solution.
Comments.UserDisplayName: Nitin
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-04-13T12:23:18.230Z
Comments.Text: What are you findings so far? What have you tried? What is your knowledge of AI/ML? Have you heard of classification? Do you have dataset? Do you have a labelled dataset? These are all questions that should have been answered in your post to make it a good post.
Comments.UserDisplayName: nbro
Tags: <deep-learning><natural-language-processing>
Title: Identify "actionable" intents in email messages
UserDisplayName: convergence
ThreadText: # Identify "actionable" intents in email messages
  
  (Asked by: username_0 on 2017-07-18)
  
  I am trying to build a model which would take an email message (in English, extracted subject, and body of the email) and identify if it has a question, request or a proposal. Basically, I would like to see the mails that I've not replied but needs a reply. The model can be used as a "filter" in an email client.
  
  What is the best way to go about it?
  
  Related Work:
  
  [Parakweet Lab's Email Intent Data Set](https://github.com/ParakweetLabs/EmailIntentDataSet/wiki)
  
  [Learning to Classify Email into "Speech Acts"](https://www.cs.cmu.edu/~tom/EMNLP2004_final.pdf)
  
  **Tags:** deep-learning, natural-language-processing
  > **Comments:**
  > **username_1**: When you're trying to implement some project or working on a project model,you need to give a snippet of what you're working. In order to get effective feedback from the community.For your information check through the [guide lines](https://ai.stackexchange.com/help)  
  > **username_0**: @quintumnia edited and gave a bit more detail.  
  > **username_2**: Am working on a similar use case. Could you implement? If yes, please share the solution.  
  > **username_3**: What are you findings so far? What have you tried? What is your knowledge of AI/ML? Have you heard of classification? Do you have dataset? Do you have a labelled dataset? These are all questions that should have been answered in your post to make it a good post.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_4 on 2018-03-18. Score: 1
  
  This is a categorization problem, not unlike a spam filter. Instead of flagging an email as spam/not-spam, you are flagging whether it has one of the action categories that you have described.
  
  You'll need to start by assembling a training corpus of example email messages and labeling each example to identify which (maybe multiple) of your categories, if any, are actually present in that email.
  
  Next, pre-process that data to extract features for each message. Examples of typical features include word (or n-gram) counts/frequencies (bag of words). As a shortcut, you might to include as a feature a boolean indicating the presence or absence of a particular word or phrase that you suspect will be predictive of one or more categories. Techniques such as stemming can help reduce the number of words/n-grams being used (often increasing accuracy).
  
  Once you have a dataset that consists of features and labels for each training email (possibly breaking this set up into subsets for training, cross-validation, and testing), you'll want to apply a supervised classification algorithm. You might start with linear classifiers such as logistic regression or SVMs, and if you're unsatisfied with the resulting accuracy then you could advance to neural techniques.
  
  > **Comments:**
  > **username_5**: "straightforward" ? too optimistic  
  > **username_4**: Optimistic perhaps, but all is relative. :-) I'll try to expand a bit more on high level steps. As a note to beginners who want to dig deeper into implementation, Andrew Ng's Coursera course might be a good starting point.  
  
  ---
  
  