@gonhaddock Addressing the misspelled terms can also be done in various other ways. Advanced KNIME users like you might want to check out this thread and somehow find a way to integrate it into KNIME by creating a new component. It’s a possibility.
Hehehehe the differences can be subtle sometimes, but they’re still there!
Hi everyone,
here is my solution.
Probably low perfomance on large datasets.
Not used engine rule.
Hi here is my solution. I used some nodes that are new for me and it results in tags.
I would not be happy if I would need to use these tags as input for any following workflows so I’m happy to see all your solutions have a different approach.
@badger101 That’s a quite common pattern for free and open source nodes. We developers who create such nodes in our spare time with limited resources usually don’t want to spend much money on expensive certificates to sign the nodes. So if you know the source (URL, HTTPS, reliable vendor), you can safely install such nodes.
NodePit says this:
You might see a warning regarding missing signing/certificates. You can safely ignore this. Most community developers of free and open source products do not sign their products to avoid large costs for acquiring certificates.
The Spellchecker Nodes are developed by @qqilihq who also develops the Palladian Nodes and the Selenium Nodes. So I would consider them safe
Best regards,
Daniel
Thank you so much for the explanation. Really appreciate it. I was busy these last 2 days creating tools to address my previous concern, which now is not a concern anymore based from what you just wrote.
I’m deciding now whether I should keep these to myself, or to publish them on the hub as an alternative:
Thanks for the great explanation, @danielesser and thanks for rising that question @badger101. Here’s some additional 5 cents about that topic (from the maker of the Spellchecker nodes perspective):
We (NodePit and Selenium Nodes) currently do not sign the jars (no matter if it’s for free or for paid nodes). Signing them gives little objective security benefits but it’s a big hassle on top of the plenty of big hassles one faces in the Eclipse/KNIME development ecosystem (and which we rather invest in building great software).
Why no security benefits? As seen above, most users do not really know what “signing” exactly means. Facts: It will not protect you from bad/malevolent software. There is no external entity involved which “validates”, “authenticates” or “reviews” the “signed” software at all. At the end, the main reason for signing the software would just be about getting rid of that annoying dialog (which is definitely frightening).
So. Should you “trust” the Spellchecker nodes? This question I cannot answer
Should you make your decision based on that unsigned content dialog? I think no.
By the way: For any questions about these nodes, don’t hesitate to get in touch!
–Philipp
@qqilihq Update: I just downloaded and tested your node. It worked remarkably well! Will definitely keep it as my permanent collection to use when it matters
Whoah! This is super cool and insightful!
Hey
Here’s my solution.
I tried a few of the purpose build nodes, however I wasn’t pleased with the result. The most accurate that I got, was using a simple contains().
/MMU
MY take on this weeks challenge.
i am just starting here , great interface and forum overall . Motivating challenges
Thanks for your feedback! We’re looking forward to seeing your solutions!
As always on Tuesdays, here’s our solution to last week’s #justknimeit challenge!
A heavy use of similarity search and regular expressions, no spellchecking.
Thanks for the participation! Lots of great insights this time around.
We hope to see you tomorrow for a new challenge!
tthank you for the welcoming appreciation
Hello everyone !
My take on this challenge.
I’ve build a sort of scoring matrix to decide to which “category” the Finding fits better. I’ve done this by using the String Similarity Node from NodePit.
My workflow basically split each row, word by word, and then calculates the Leventshtein Similarity between each word and the Possible Finding.
After a few transformations, I search for the max similarity percentage for each Finding, so we can have the final classifications:
There are a couple of cases in where the Finding have two keyword in its description. For example this line could be either coating or missing
I see that some of you have a spelling analysis also, I did not do this, just to keep it simple.
See y’all on the next one !
Interesting to learn new about the similarity search node, although, I used it before for a different algorithm. Thanks for the challenge. Nevertheless, 5 nodes.
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