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This is more of a guideline question rather than a technical query. I am looking to create a classification model that classifies documents based on a specific list of strings. However, as it turns out from the data, that that output is more useful when you pass labelled intents / topics, instead of allowing the model to guess. Based on my research so far, I found that Bertopic might be the right way to achieve this as it allows guided topic modeling but the only caveat is that the guided topics should contain words of similar meaning (see link below).

It might be more clear from the example below on what I want to achieve. Suppose we have a chat text extract from a conversation between a customer and store associate, below is what the customer asking for.

Hi, the product I purchased from your store is defective and I would like to get it replaced or refunded. Here is the receipt number and other details...

If my list of intended labels is as follows: ['defective or damaged', 'refund request', 'wrong label description',...], we can see that the above extract qualifies into 'defective or damaged' and 'refund request'. For the sake of simplicity, I would pick the one that model returns with highest score so that we only have 1 label per request. Now, if my data does not have these labels defined, I may be able to use zero-shot classification to "best guess" the intent from this list. However, as I understand the use of zero-shot classification or even the guided topic modeling of BERTopic, the categories that I want above may not be derived since the individual words in those categories do not mean that same.

For example, in BERTopic, an intended classified label could be like ["space", "launch", "orbit", "lunar"] for a "Space Related" topic, but in my case let us say for the 3rd label it would be ["wrong", "label", "description"] which would not be best suited as it would try to find all records that have mentions of wrong address, wrong department, wrong color etc., so I am essentially looking for a combination of those 3 words in the context. Also, those 3 words may not always be together or in same order. For example, in this sentence -

The item had description which was labelled incorrectly.

This same challenge would be for zero-shot classification where the labels are expected to be one word or a combination of words that mean the same thing. Let me know if this clarifies the question more or if I can help further clarify it.

Approach mentioned above:

https://maartengr.github.io/BERTopic/getting_started/guided/guided.html#semi-supervised-topic-modeling

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