SYSTEM AND METHOD FOR LEARNING WORD EMBEDDINGS USING NEURAL LANGUAGE MODELS
First Claim
1. A method of learning natural language word associations using a neural network architecture, comprising processor implemented steps of:
- storing data defining a word dictionary comprising words identified from training data consisting a plurality of sequences of associated words;
selecting a predefined number of data samples from the training data, the selected data samples defining positive examples of word associations;
generating a predefined number of negative samples for each selected data sample, the negative samples defining negative examples of word associations, wherein the number of negative samples generated for each data sample is a statistically small proportion of the number of words in the word dictionary; and
training a neural language model using said data samples and said generated negative samples.
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Abstract
A system and method are provided for learning natural language word associations using a neural network architecture. A word dictionary comprises words identified from training data consisting a plurality of sequences of associated words. A neural language model is trained using data samples selected from the training data defining positive examples of word associations, and a statistically small number of negative samples defining negative examples of word associations that are generated from each selected data sample. A system and method of predicting a word association is also provided, using a word association matrix including data defining representations of words in a word dictionary derived from a trained neural language model, whereby a word association query is resolved without applying a word position-dependent weighting.
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Citations
41 Claims
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1. A method of learning natural language word associations using a neural network architecture, comprising processor implemented steps of:
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storing data defining a word dictionary comprising words identified from training data consisting a plurality of sequences of associated words; selecting a predefined number of data samples from the training data, the selected data samples defining positive examples of word associations; generating a predefined number of negative samples for each selected data sample, the negative samples defining negative examples of word associations, wherein the number of negative samples generated for each data sample is a statistically small proportion of the number of words in the word dictionary; and training a neural language model using said data samples and said generated negative samples. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 38, 39, 40)
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19. A method of predicting a word association between words in a word dictionary, comprising processor implemented steps of:
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storing data defining a word association matrix including a plurality of vectors, each vector defining a representation of a word derived from a trained neural language model; receiving a plurality of query words; retrieving the associated representations of the query words from the word association matrix; calculating a candidate representation based on the retrieved representations; and determining at least one word in the word dictionary that matches the candidate representation, wherein the determination is made based on the word association matrix and without applying a word position-dependent weighting. - View Dependent Claims (20, 21, 22, 23, 24, 35, 41)
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36. A system for learning natural language word associations using a neural network architecture, comprising one or more processors configured to:
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store data defining a word dictionary comprising words identified from training data consisting of a plurality of sequences of associated words; select a predefined number of data samples from the training data, the selected data samples defining positive examples of word associations; generate a predefined number of negative samples for each selected data sample, the negative samples defining negative examples of word associations, wherein the number of negative samples generated for each data sample is a statistically small proportion of the number of wherein the number of negative samples generated for each data sample is a statistically small proportion of the number of words in the word dictionary; and train a neural language model using said data samples and said generated negative samples.
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37. A data processing system for resolving a word similarity query, comprising one or more processors configured to:
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store data defining a word association matrix including a plurality of vectors, each vector defining a representation of a word derived from a trained neural language model; receive a plurality of query words; retrieve the associated representations of the query words from the word association matrix; calculate a candidate representation based on the retrieved representations; and determine at least one word that matches the candidate representation, wherein the determination is made based on the word association matrix and without applying a word position-dependent weighting.
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Specification