Node.js interface to the Google word2vec tool
This is a Node.js interface to the word2vec tool developed at Google Research for "efficient implementation of the continuous bag-of-words and skip-gram architectures for computing vector representations of words", which can be used in a variety of NLP tasks. For further information about the word2vec project, consult https://code.google.com/p/word2vec/.
Currently, node-word2vec is ONLY supported for Unix operating systems.
Install it via npm:
npm install word2vecTo use it inside Node.js, require the module as follows:
var w2v = require( 'word2vec' );For applications where it is important that certain pairs of words are treated as a single term (e.g. "Barack Obama" or "New York" should be treated as one word), the text corpora used for training should be pre-processed via the word2phrases function. Words which frequently occur next to each other will be concatenated via an underscore, e.g. the words "New" and "York" if following next to each other might be transformed to a single word "New_York".
Internally, this function calls the C command line application of the Google word2vec project. This allows it to make use of multi-threading and preserves the efficiency of the original C code. It processes the texts given by the input text document, writing the output to a file with the name given by output.
The params parameter expects a JS object optionally containing some of the following keys and associated values. If they are not supplied, the default values are used.
| Key | Description | Default Value |
|---|---|---|
| minCount | discard words appearing less than minCount times | 5 |
| threshold | determines the number of phrases, higher value means less phrases | 100 |
| debug | sets debug mode | 2 |
| silent | sets whether any output should be printed to the console | false |
After successful execution, the supplied callback function is invoked. It receives the number of the exit code as its first parameter.
This function calls Google's word2vec command line application and finds vector representations for the words in the input training corpus, writing the results to the output file. The output can then be loaded into node via the loadModel function, which exposes several methods to interact with the learned vector representations of the words.
The params parameter expects a JS object optionally containing some of the following keys and associated values. For those missing, the default values are used:
| Key | Description | Default Value |
|---|---|---|
| size | sets the size of word vectors | 100 |
| window | sets maximal skip length between words | 5 |
| sample | sets threshold for occurrence of words. Those that appear with higher frequency in the training data will be randomly down-sampled; useful range is (0, 1e-5) | 1e-3 |
| hs | 1 = use Hierarchical Softmax | 0 |
| negative | number of negative examples; common values are 3 - 10 (0 = not used) | 5 |
| threads | number of used threads | 12 |
| iter | number of training iterations | 5 |
| minCount | This will discard words that appear less than minCount times | 5 |
| alpha | sets the starting learning rate | 0.025 for skip-gram and 0.05 for CBOW |
| classes | output word classes rather than word vectors | 0 (vectors are written) |
| debug | sets debug mode | 2 |
| binary | save the resulting vectors in binary mode | 0 (off) |
| saveVocab | the vocabulary will be saved to saveVocab value | |
| readVocab | the vocabulary will be read from readVocab value , not constructed from the training data | |
| cbow | use the continuous bag of words model | 1 (use 0 for skip-gram model) |
| silent | sets whether any output should be printed to the console | false |
After successful execution, the supplied callback function is invoked. It receives the number of the exit code as its first parameter.
This is the main function of the package, which loads a saved model file containing vector representations of words into memory. Such a file can be created by using the word2vec function. After the file is successfully loaded, the supplied callback function is fired, which following conventions has two parameters: err and model. If everything runs smoothly and no error occured, the first argument should be null. The model parameter is a model object holding all data and exposing the properties and methods explained in the Model Object section.
Example:
w2v.loadModel( './vectors.txt', function( error, model ) {
console.log( model );
});Sample Output:
{
getVectors: [Function],
distance: [Function: distance],
analogy: [Function: analogy],
words: '98331',
size: '200'
}Number of unique words in the training corpus.
Length of the learned word vectors.
Calculates the word similarity between word1 and word2.
Example:
model.similarity( 'ham', 'cheese' );Sample Output:
0.4907762118841032Calculates the cosine distance between the supplied phrase (a string which is internally converted to an Array of words, which result in a phrase vector) and the other word vectors of the vocabulary. Returned are the number words with the highest similarity to the supplied phrase. If number is not supplied, by default the 40 highest scoring words are returned. If none of the words in the phrase appears in the dictionary, the function returns null. In all other cases, unknown words will be dropped in the computation of the cosine distance.
Example:
model.mostSimilar( 'switzerland', 20 );Sample Output:
[
{ word: 'chur', dist: 0.6070252929307018 },
{ word: 'ticino', dist: 0.6049085549621765 },
{ word: 'bern', dist: 0.6001648890419077 },
{ word: 'cantons', dist: 0.5822226582323267 },
{ word: 'z_rich', dist: 0.5671853621346818 },
{ word: 'iceland_norway', dist: 0.5651901750812693 },
{ word: 'aargau', dist: 0.5590524831511438 },
{ word: 'aarau', dist: 0.555220055372284 },
{ word: 'zurich', dist: 0.5401119092258485 },
{ word: 'berne', dist: 0.5391358099043649 },
{ word: 'zug', dist: 0.5375590160292268 },
{ word: 'swiss_confederation', dist: 0.5365824598661265 },
{ word: 'germany', dist: 0.5337325187293028 },
{ word: 'italy', dist: 0.5309218588704736 },
{ word: 'alsace_lorraine', dist: 0.5270204106304165 },
{ word: 'belgium_denmark', dist: 0.5247942780963807 },
{ word: 'sweden_finland', dist: 0.5241634037188426 },
{ word: 'canton', dist: 0.5212495170066538 },
{ word: 'anterselva', dist: 0.5186651140386938 },
{ word: 'belgium', dist: 0.5150383129735169 }
]For a pair of words in a relationship such as man and king, this function tries to find the term which stands in an analogous relationship to the supplied word. If number is not supplied, by default the 40 highest-scoring results are returned.
Example:
model.analogy( 'woman', [ 'man', 'king' ], 10 );Sample Output:
[
{ word: 'queen', dist: 0.5607083309028658 },
{ word: 'queen_consort', dist: 0.510974781496456 },
{ word: 'crowned_king', dist: 0.5060923120115347 },
{ word: 'isabella', dist: 0.49319425034513376 },
{ word: 'matilda', dist: 0.4931204901924969 },
{ word: 'dagmar', dist: 0.4910608716969606 },
{ word: 'sibylla', dist: 0.4832698899279795 },
{ word: 'died_childless', dist: 0.47957251302898396 },
{ word: 'charles_viii', dist: 0.4775804990655765 },
{ word: 'melisende', dist: 0.47663194967001704 }
]Returns the learned vector representations for the input word. If word does not exist in the vocabulary, the function returns null.
Example:
model.getVector( 'king' );Sample Output:
{
word: 'king',
values: [
0.006371254151248689,
-0.04533821363410406,
0.1589142808632736,
...
0.042080221123209825,
-0.038347102017109225
]
}Returns the learned vector representations for the supplied words. If words is undefined, i.e. the function is evoked without passing it any arguments, it returns the vectors for all learned words. The returned value is an array of objects which are instances of the class WordVec.
Example:
model.getVectors( [ 'king', 'queen', 'boy', 'girl' ] );Sample Output:
[
{
word: 'king',
values: [
0.006371254151248689,
-0.04533821363410406,
0.1589142808632736,
...
0.042080221123209825,
-0.038347102017109225
]
},
{
word: 'queen',
values: [
0.014399041122817985,
-0.000026896638109750347,
0.20398248693190596,
...
-0.05329081648586445,
-0.012556868376422963
]
},
{
word: 'girl',
values: [
-0.1247347144692245,
0.03834108759049417,
-0.022911846734360187,
...
-0.0798994867922872,
-0.11387393949666696
]
},
{
word: 'boy',
values: [
-0.05436531234037158,
0.008874993957578164,
-0.06711992414442335,
...
0.05673998568026764,
-0.04885347925837509
]
}
]Returns the word which has the closest vector representation to the input vec. The function expects a word vector, either an instance of constructor WordVector or an array of Number values of length size. It returns the word in the vocabulary for which the distance between its vector and the supplied input vector is lowest.
Example:
model.getNearestWord( model.getVector('empire') );Sample Output:
{ word: 'empire', dist: 1.0000000000000002 }Returns the words whose vector representations are closest to input vec. The first parameter of the function expects a word vector, either an instance of constructor WordVector or an array of Number values of length size. The second parameter, number, is optional and specifies the number of returned words. If not supplied, a default value of 10 is used.
Example:
model.getNearestWords( model.getVector( 'man' ), 3 )Sample Output:
[
{ word: 'man', dist: 1.0000000000000002 },
{ word: 'woman', dist: 0.5731114915085445 },
{ word: 'boy', dist: 0.49110060323870924 }
]The word in the vocabulary.
The learned vector representation for the word, an array of length size.
Adds the vector of the input wordVector to the vector .values.
Subtracts the vector of the input wordVector to the vector .values.
Run tests via the command npm test
Clone the git repository with the command
$ git clone https://github.com/Planeshifter/node-word2vec.gitChange into the project directory and compile the C source files via
$ cd node-word2vec
$ make --directory=src