NLP using word2vec for Donor Matching

In our previous post, we noted how 1 and 2 grams from a query mission can be matched to donor mission statements to find an appropriate donor organization. We performed some text cleaning and used tfidf as a metric for weighting the more important words. The final results showed some issues with this method – like the word ‘heart’ in “Isreal at Heart” being matched to “heart conditions”. When we know that most words will have a context associated with their usage and the meaning of the word changes according to its context, how do we still match missions by matching words?

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Using NLP to find the perfect donor match – 1

One of the difficulties of starting a non-profit is finding donors and funding agencies. We’ve seen first hand the challenges involved! With so many different foundations with a wide variety of mission statements, how do we find one that is most closely aligned with our goals?

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