Removing junk sentences I have transcripts of phone calls with customers and agents. I'm trying to find promises which were made by an agent to a customer. 

I already did punctuation restoration. But there are a lot of sentences that don't have any sense. I would like to remove them from the transcript. Most of them are just a set of not connected words. 
I wonder what approach is the best for this task?

My ideas are:

• Use tf idf and word2vec to create vectors from all sentences. After that we can do some kind of anomaly detection e.g. look for and delete vectors that are highly deviated from most other vectors.

• Spam filters. Maybe is it possible to apply spam filters for this task?

• Crate some pattern of part of speech tags that proper sentence must include. For example, any good sentence must include noun + verb. Or we can use for example dependency tokens from spacy.

update

Example of a sentence that I want to keep:


  There's no charge once sent that you'll get a ups tracking number.


Example of a junk sentence:


  Kinder pr just have to type it in again, clock drives bethel.


Another junk sentence:


  Just so you have it on and said this is regarding that.
I would suggest training your own custom Dialog Act Classification Model. Detecting a dialog act is a Natural Language Understanding (NLU) problem. Using a dialog act classification model you can detect if a sentence is used for "greeting", "question", "opinion", "promise", etc.

Detailed approach:

Use a pre-trained language model (for example BERT), to train your own Dialog act classification model. You can create your own custom Dialog act class based on your requirement (as you want to remove specific types of sentences). You can class them as junk or use "greeting", "question", "opinion", etc as dialog class.

After you are done with the training you can loop through the list of sentences and filter them based on their predicted classes. 


  You might have to research if there are similar corpus which you can
  use or have to manually label and then train the model. It will be a more reliable approach as compared to the word2vec or rule-based POS approach.


For more details on state-of-the-art approaches and dialogue act corpus follow this link. If you have no idea how to use a language model you can use the transformers library from huggingface. I hope you find this helpful.