Machine Learning, Tasting, Wine Words
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Tasting Algos, Spring I.

You shall know a word by the company it keeps (Firth, J. R.)

It’s reasonable to believe that wine words which occur in similar contexts have related meanings for the tasters who wrote them down.

Join us on our machine assisted journey to explore the unplumbed depths of wine writing with natural language processing (NLP) techniques.

As machine learning meets human taste, let’s strive to make the symbiosis fruitful: with NLP algorithms and computing power at our fingertips, here’s an opportunity to discover more about how we record sensory impressions with words.

We’ve trained neural network models on a large corpus of wine notes to recognize words by the company they keep. The results are fascinating.

It’s spring-time, so let’s start by investigating a couple of fresh, uplifting, seasonal descriptors that come to mind naturally.

Each word comes with its top 10 contextual associates picked out by our machine assistant.  The score (cosine similarity) indicates how close the association is.

Blossoms

  • ‘peaches’, 0.64
  • ‘wax’, 0.64
  • ‘poached’, 0.62,
  • ‘bursts’, 0.61
  • ‘hazelnuts’, 0.61
  • ‘apples’, 0.61
  • ‘buttered’, 0.60
  • ‘apricots’, 0.60
  • ‘candle’, 0.60
  • ‘oranges’, 0.59

Racy

  • ‘vibrant’, 0.73
  • ‘precise’, 0.72
  • ‘sleek’, 0.64
  • ‘punchy’, 0.64
  • ‘zesty’, 0.63
  • ‘crisp’, 0.63
  • ‘linear’, 0.62
  • ‘detailed’, 0.62
  • ‘energetic’, 0.62
  • ‘minerally’, 0.61

Breezy

  • ‘sea’, 0.87
  • ‘oyster’, 0.76
  • ‘resin’, 0.75
  • ‘shell’, 0.74
  • ‘rosemary’, 0.73
  • ‘iodine’, 0.69
  • ‘salt’, 0.68
  • ‘musk’, 0.65
  • ‘clove’, 0.64
  • ‘quinine’, 0.64

Suggest a word or a theme and we’ll include the company it keeps in our next post!

If you’re a keen wine writer, may we interest you in an analysis of your corpus? How has your vocabulary changed over time? Are there seasonal patterns? How does the size of your dictionary stack up to those of your fellow critics?

MK@WineQuant

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