Machine Learning for Long Sticky Note Segmentation

• With an approach that estimates for each word whether or not to erase it and whether or not to separate it with the right,
• There’s an approach to generate a column containing delimited tokens with BERT or something like that.

I was thinking of the former, because I don’t want to make it too heavy to run on a server, but this approach would be ruined the moment I try to support rewriting.

Should we try and not avoid the latter with “it might be heavy?”

What should happen to teacher data?

• So far, we’ve documented sporadic examples of bad or human-induced splits.
• If you want to put it in BERT, maybe a column of delimited token ticks.
• We thought it would be difficult to express rewriting in the current rule-based version, but we can do string substitution in the preprocessing
• Then after that, it’s usually engraved on the token.
• Worst case, only rule-based pre-write can be left in the rule base.

2021/3/29

• Spit out the results of the split with the current rule-based one to a file.

• Put an x on a bad split result and an o on a good split result

  • You don’t have to put anything on.
  • The ones with nothing on them can be o as being so-so acceptable.

• At least it’s better than the current “failure cases are in the form of memos with no formatting”.

• Hopefully the bad splits will be reduced when the rule is modified.

• I want to merge outputs.

• Machine learning?

  • I think it could be within the scope of parameter adjustment.

• Even if we feed BERT in the future, I’d like to wait until after a few hundred data have been collected.

• If you keep the data in this form, you can split it up and use it as teacher data if it does not contain “bad examples”.

• I checked the code.

  • JSON output for regression testing to prevent unexpected breakage.
  • This one has a list of “split results.”
  • It’s a list, so you can’t mark “this result is bad”.
  • Regression testing itself will continue to be useful
  • You can have a separate ✅ dictionary.
  • Right now it’s rule-based, so I’m chopping in order of highest score, but if the score is 0.5 or higher, I’ll definitely split it up, and if something is still too long, should I chop it up separately?

• Assistance in dividing a long document into stickies: an example of a bad division

2021-03-30

• I get the feeling that the type of case particle alone doesn’t seem to determine which case particle feels natural to me when I need to split with any of them.
  • I wonder if it depends on the type of case particle next to it and the distance…

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