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Knowing this you can filter/ match the question to your predefined answers much better than before.

from import pos_tag def pos_tag_sentence(sentence): """Takes a list of words and returns their matching part of speech""" default_tagger = load(_POS_TAGGER) train_model = g.train_model # the custom model as a dictionary tagger =

Then the question arises as to What the bots are going to learn, How, From Whom, etc.

In other words, how would you be able to apply necessary Filters to what the bot is "taking in", learning, storing for later use and will it be appropriate for my intended purpose? To greatly simplify things, you might wish to consider a scripted bot like the ones I mentioned earlier.

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Open another terminal window so you have two open inside the /base folder.

Or, more commonly, it is driven using intelligent rules (i.e. The term chatbot is synonymous with text conversation but is growing quickly through voice communication… Consumers spend lots of time using messaging applications (more than they spend on social media).

Therefore, messaging applications are currently the most popular way companies deliver chatbot experiences to consumers.

If the FAQ feature is important and in a limited domain it's probably best to just use something like AIML and do as many variations on the phrase as you can think of - then by studying chat logs you will see other patterns that could work too, that you had not thought of. I absolutely agree with Freddy's suggestions with regard to comparing "self-learning" bots to Scripted bots like Rive Script, Chat Script or an AIML based bot.

The Self-learners might not be able to present what they've learned in a manner that's consistent with what or how you'd rather have them present it.

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