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Building A Taxonomy For A Quotations Reference Application

Hachette Book Group has a mobile app for Bartlett's Familiar Quotations, one of the world's most recognized classic reference books.  This project proved to be an extraordinarily novel application of taxonomy best practices.

Nearly twenty thousand quotations were analyzed for their aboutness, followed by a careful evaluation of the result. In Bartlett’s Familiar Quotations, Eighteenth Edition, our indexers found 1134 quotations that were in some nontrivial way about “love” -- and this total doesn’t include quotations about love songs, marriage without love, or the deaths of lovers and loved ones. According to our calculations, 1134 quotations needed to be tagged with the keyword “love.”

Bartlett’s editors at Little, Brown and Company had never faced this challenge before. For over a century and a half, their anthologies of quotations had been printed with only a concordance. Quotes containing the precise word love were isolated from those containing variations like lovers, beloved, andloving, not to mention literary phenomena like Luve and loverly. Our highly interpretive approach, which required a team of specialized indexers, involved reading each quote for meaning and understanding. Sometimes the word never appeared: I feel again a spark of that ancient flame. Absence makes the heart grow fonder. I am my beloved’s, and his desire is toward me. Leave a log in the water as long as you like: it will never be a crocodile. And only through hard work were we able to surface these quotations to those searching for love. Or lovers.

But best practices dictate that balance matters. We found only two quotations about applause, two about begging, and four about complaining. We badly needed to build subcategories for our 1134 love items. Thankfully, our teamwork approach functioned much like a card sort, and ultimately we settled on approximately 70 classifications for our lovely quotations, from young love and unrequited love to the love of learning and the love of self, and cross-referenced related topics like romance and marriage.

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Earley Information Science Team
Earley Information Science Team
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