word2vec

Word Vector functions based on word2vec.

Data

Model is trained on theon part of Google News dataset (about 100 billion words).

The model contains 300-dimensional vectors for 3 million words and phrases.

Usage

There is multiple functions available to query. The way this works is that you send one (or mutiple as a list) of json queries, where each query is defined as:

vector: Get vectors for specific words
distance: Calculate distances between the combinations of words
closest: Get the closest n words and distances to a vector
similar: Get the most similar n words and distances to one words
analogy: Make word analogies based on positive and negative feedback

1. Vector

Returns the vector that represent a word.

Parameter	Description
words	List of words

Example:

{
    "vector":{
        "words":[
            "dog",
            "cat"
        ]
    }
}

Output:

{
    "cat": [0.001, 0.06, ...],
    "dog": [0.02, 0.002, ...]
}

2. Distance

Returns the distance between all the combinations (r=2) of multiple words.

Parameter	Description
words	List of words

Example:

{
    "distance":{
        "words":["dog", "cat", "fish", "bird"]
    }
}

Output:

[
    ["dog",  "cat",  0.7609457299277049],
    ["dog",  "fish", 0.2331458338686121],
    ["dog",  "bird", 0.4504405095095247],
    ["cat",  "fish", 0.32425707394406234],
    ["cat",  "bird", 0.5146262971059316],
    ["fish", "bird", 0.49408521392145377]
]

3. Closest

Get the n (default=10) closest words and respective distances to a vector.

Parameter	Description
vector	vector of size 300 as list of numbers
n	(default=10) Top N items to return

Example:

{
    "closest": {
        "vector": [0.02, 0.006, ...], "n": 10
    }
}

Output:

For the vector that represents "dog", you can get this using query #1.

[
    ["dogs",    0.8680489915680536],
    ["puppy",   0.8106428352360295],
    ["pit_bull",0.7803960342611623],
    ["pooch",   0.7627376737257423],
    ["cat",     0.7609457299277049],
    ["golden_retriever",0.7500902073008441],
    ["German_shepherd", 0.7465174355312222],
    ["Rottweiler",      0.7437614773466921],
    ["beagle",  0.7418621650220392],
    ["pup",     0.7406910838103349]
]

4. Similar

Get the n (default=10) similar words and distances to one

Parameter	Description
word	Word to calculate distances
n	(default=10) Top N items to return

Example:

{
    "similar": {
        "word": "dog", "n": 3
    }
}

Output:

[
    ["dogs",  0.8680489915680536],
    ["puppy", 0.8106428352360294],
    ["pit_bull",  0.7803960342611623]
]

5. Analogy

Parameter	Description
pos	List of positive words
neg	List of negative words
n	(default=10) Top N items to return

Compute an analogy based positive words and negative words, for example: king - man + woman = queen

{
    "analogy":  {
        "pos": ["king", "woman"],
        "neg": ["man"],
        "n": 5
    }
}

[
    ["queen",    0.26689835890816027],
    ["monarch",  0.23208332083114688],
    ["princess", 0.22131306095983244],
    ["crown_prince", 0.20620359683335132],
    ["prince",   0.20162396428463877]
]

Multiple queries

You can query multiple times per request by just sending a list of queries:

[
    {"vector":  { "words": ["dog", "cat"] } },
    {"distance": { "words": ["dog", "cat", "fish"]} },
    {"distance": { "words": ["dog", "cat", "fish"]} }
]

The output will be a list with the response in the same order as the queries.

danielfrg / word2vec / 0.3.2

Estimate Pricing

10k credits ($1.03 USD)