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You need to discover the node ancestors recursively and then expand them on by one:

We might be tempted to do a regex replacement here, but that would basically always leave open edge cases, as @Wiktor has correctly pointed out in a comment below. Instead, a more foolproof approach is to use re.findall and simply extract every tuple with does not end in 'page'. Here is an example:

Okay so here is an example on how you save to/ load from the documents folder.

First of all make sure that you object MatchInfo conforms to this protocol.

Feed title can be accessed in this case with feed.feed.title:

If the Jupyter kernel is dying without a clear error message, you are likely running out of memory.

There may be more information logged to the console where you started the Jupyter server. If you expand you question to include any info there, as well as details about the model you've loaded (size on disk) and system you're running on (especially, RAM available), it may be possible to make other suggestions.

Also:

Whereas gensim-3.8.3 requires a big new increment of RAM when the first .most_similar() call is made, the gensim-4.0.0beta pre-release only needs a much-smaller increment at that time - so it is far more likely that if a model succeeds in loading, you should also be able to get .most_similar() results. So it would also be useful to know:

• How did you install the gensim-4.0.0beta, and did you confirm that's the version actually used by your notebook kernel's environment?
• Are you certain that the prior steps (such as loading) have succeeded, and that it's only & exactly the most_similar() that's triggering the failure? (Is it in a separate cell, and before attempting the most_similar() can you query other aspects of the model, such as its length or whether it contains certain words, successfully?)

I would suggest a following pipeline:

1. Construct a training set from existing dataset of synonyms and antonyms (taken e.g. from the Wordnet thesaurus). You'll need to craft negative examples carefully.
2. Take a pretrained model such as BERT and fine-tune it on your tasks. If you choose BERT, it should be probably BertForNextSentencePrediction where you use your words/prhases instead of sentences, and predict 1 if they are synonyms and 0 if they are not; same for antonyms.

Change:

The way you do this depends on your interpreter and how you are using it. If your interpreter only supports AIML 1, you can display your image using HTML tags.

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