Companion Words

Discover the words that are most frequently used in conversation with each other. Trained on billions of tweets with over 1 million unique words.

Use Cases

Recommendation engine for related searches
Discover the name for your next project
Suggest relevant words in a messenger or email client
Serve relevant ads based on a user's interests

Input Scheme

The input should contain an array of words and an integer limit (between 1-100). The limit specifies how many similar words we want per input word. Note, the app is case-insensitive i.e. cat and CAT will have the same output.

{
  "words": ["blue", "twitter", "fkdsjfsa"],
  "limit": 5
}

Output Scheme

The output will map each input word to an array of words with high relevance. Notice how fkdsjfsa is missing in the output; input words which are not found in the app's vocabulary are skipped.

{
  "words": 
    { 
      "blue": [
        {"label": "yellow", "score": 0.96}, 
        {"label": "red", "score": 0.95}, 
        {"label": "green", "score": 0.95}, 
        {"label": "purple", "score": 0.95}, 
        {"label": "black", "score": 0.94}
      ], 
      "twitter": [
        {"label": "facebook", "score": 0.95}, 
        {"label": "tweet", "score": 0.94}, 
        {"label": "fb", "score": 0.93}, 
        {"label": "instagram", "score": 0.91}, 
        {"label": "chat", "score": 0.9}
      ]
    }
}

Training

The model was trained by Stanford's NLP group on a dataset of 2 billion, uncased tweets (27 billion tokens with 1.2 million distinct words).

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