Answers.Body: <p>If you're fine training a model of your own, I would suggest try building a NER prediction model on your data. It would help to start with a certain type of data which follows similar pattern throughout. Once you're confident, you can extend it to many more patterns.</p>
  <p>Or if you're lucky and find <a href="https://huggingface.co/models?search=ner" rel="nofollow noreferrer">pre-trained NER models</a> to be able to identify the event-date relationships already, you can just use them out-of-the-box.</p>
  <p>Interestingly, I tried some <a href="https://huggingface.co/models?pipeline_tag=question-answering&amp;sort=downloads" rel="nofollow noreferrer">QA models</a> to simply ask the event &amp; time of event given some context, and <a href="https://huggingface.co/deepset/roberta-base-squad2" rel="nofollow noreferrer">deepset/roberta-base-squad2</a> works pretty well for simple examples.</p>
  <blockquote>
  <p>Context: I have a flight to catch at 6 AM tomorrow.</p>
  <p>Question: What is the topic of event?<br />
  Answer: a flight to catch at 6 AM tomorrow</p>
  <p>What is the date of event?<br />
  6 AM tomorrow</p>
  </blockquote>
  <blockquote>
  <p>Context: The next set of features are scheduled to be discussed at meeting on 25th Dec 2022.</p>
  <p>Question: What is the topic of event?<br />
  Answer: The next set of features</p>
  <p>Question: What is the date of event?<br />
  Answer: 25th Dec 2022</p>
  </blockquote>
  <p>Of course your data could be very different, but the pre-trained models should be a good starting point for ideas.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-10-21T18:20:35.693Z
Answers.LastActivityDate: 2022-10-21T18:20:35.693Z
Answers.UserDisplayName: Ashwani Yadav
Body: <p>I am new to this community and not even too sure if this is the right place for such a question.</p>
  <p>I want to know how I could implement (possibly with already made solutions) a system which could take in a (possibly long winded) text file and deduct from there, events and meetings which a user could add (similar to what Siri does when it finds a possible event).</p>
  <p>Is there something for this that already exists?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-10-21T02:41:05.447Z
Comments.Text: Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer.
Comments.UserDisplayName: Community
Tags: <machine-learning><text-summarization>
Title: Can I pick up possible calendar events from a text file
UserDisplayName: Yossi Sternlicht
ThreadText: # Can I pick up possible calendar events from a text file
  
  (Asked by: username_0 on 2022-10-20)
  
  I am new to this community and not even too sure if this is the right place for such a question.
  
  I want to know how I could implement (possibly with already made solutions) a system which could take in a (possibly long winded) text file and deduct from there, events and meetings which a user could add (similar to what Siri does when it finds a possible event).
  
  Is there something for this that already exists?
  
  **Tags:** machine-learning, text-summarization
  
  > **Comments:**
  > **username_1**: Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2022-10-21. Score: 2
  
  If you're fine training a model of your own, I would suggest try building a NER prediction model on your data. It would help to start with a certain type of data which follows similar pattern throughout. Once you're confident, you can extend it to many more patterns.
  
  Or if you're lucky and find [pre-trained NER models](https://huggingface.co/models?search=ner) to be able to identify the event-date relationships already, you can just use them out-of-the-box.
  
  Interestingly, I tried some [QA models](https://huggingface.co/models?pipeline_tag=question-answering&sort=downloads) to simply ask the event & time of event given some context, and [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) works pretty well for simple examples.
  
  > Context: I have a flight to catch at 6 AM tomorrow.
  >
  > Question: What is the topic of event?  
  > Answer: a flight to catch at 6 AM tomorrow
  >
  > What is the date of event?  
  > 6 AM tomorrow
  
  > Context: The next set of features are scheduled to be discussed at meeting on 25th Dec 2022.
  >
  > Question: What is the topic of event?  
  > Answer: The next set of features
  >
  > Question: What is the date of event?  
  > Answer: 25th Dec 2022
  
  Of course your data could be very different, but the pre-trained models should be a good starting point for ideas.
  
  
  ---
  
  