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I'm trying to build a Shapley value (marginal contribution) of a text document in terms of information content, given that there are several documents on a given topic.

For example, we have 3 reports describing the ocean:

A: {The ocean is blue.}

B: {The ocean is blue and salty.}

C: {The ocean is salty and a home for fish}

If I'm correct, a first step in the algorithm is identifying a set of features (individual pieces of information): {blue, salty, a home for fish}

Moreover, say each feature has an equal weight.

Then I can compute Shapley values of 1/6 for A, 1/3 for B, 1/2 for C.

I wonder if there are well-established algorithms / papers that deal with this problem. I'm coming from economics myself (I'm familiar with Shapley values abstractly), so apologies if the question is too trivial.

Thank you!

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From an NLP perspective, the main issue is to identify and extract the terms/relations of interest from the text.

It may be easy if the sentences always appear like these simple examples: one can use pattern matching "the <entity> is <property1> [and <property2>]" (no need for ML).

However with general text it's a quite complex problem which would probably require training an ad-hoc model from annotated data. This would generally involve the tasks of Named Entity Recognition and Relation Extraction.

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