May 2, 2019

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 tf–idf 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?

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?