Neologism classification techniques with trigrams and longest common subsequences
AssigneeOracle International Corporation
InventorsMichael Malak, Luis E. Rivas, Mark Lee Kreider
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Abstract
Techniques are provided for identifying attributes associated with a neologism or an unknown word or name. Real world characteristics can be predicted for the neologism. Trigrams are identified for an input word and word embedding model vector values are calculated for the identified trigrams and entered into a matrix. Trigrams are identified for nearest names. Classification values are calculated based on the trigrams for the input word and the trigrams from the nearest names and the classification values are entered into the matrix. A convolutional neural network can process the matrix to identify one or more characteristics associated with the neologism.
Claims20
A method comprising
- receiving, by a server computer comprising a processor and a memory, an input word comprising a first string of characters;
- determining, by the server computer, a first plurality of trigrams for the input word;
- calculating, by the server computer, word embedding vector values for the determined first plurality of trigrams;
- inputting, by the server computer, the word embedding vector values in a matrix
- determining, by the server computer, a plurality of nearest names, wherein the plurality of nearest names are names having a second string of characters that are similar to the first string of characters of the input word;
- determining, by the server computer, a second plurality of trigrams for the nearest names;
- determining, by the server computer, longest common subsequences between the first plurality of trigrams of the input word and the second plurality of trigrams of the nearest names;
- calculating, by the server computer, classification values for the first plurality of trigrams;
- updating, by the server computer, the matrix to include the calculated classification values;
- and
- running, by the server computer, the updated matrix through a convolutional neural network to determine a classification of the input word.
A server computer comprising:
- a processor;
- a memory;
- a computer readable medium coupled to the processor, the computer readable medium storing instructions executable by the processor for implementing a method comprising:
- receiving an input word comprising a first string of characters;
- determining a first plurality of trigrams for the input word;
- calculating word embedding vector values for the determined first plurality of trigrams;
- inputting the word embedding vector values in a matrix
- determining a plurality of nearest names, wherein the plurality of nearest names are names having a second string of characters that are similar to the first string of characters of the input word;
- determining a second plurality of trigrams for the nearest names;
- determining longest common subsequences between the first plurality of trigrams of the input word and the second plurality of trigrams of the nearest names;
- calculating classification values for the first plurality of trigrams;
- updating the matrix to include the calculated classification values;
- and
- running the updated matrix through a convolutional neural network to determine a classification of the input word.
A non-transitory computer readable medium including instructions configured to cause one or more processors of a server computer to perform operations comprising:
- receiving an input word comprising a first string of characters;
- determining a first plurality of trigrams for the input word;
- calculating word embedding vector values for the determined first plurality of trigrams;
- inputting the word embedding vector values in a matrix
- determining a plurality of nearest names, wherein the plurality of nearest names are names having a second string of characters that are similar to the first string of characters of the input word;
- determining a second plurality of trigrams for the nearest names;
- determining longest common subsequences between the first plurality of trigrams of the input word and the second plurality of trigrams of the nearest names;
- calculating classification values for the first plurality of trigrams;
- updating the matrix to include the calculated classification values;
- and
- running the updated matrix through a convolutional neural network to determine a classification of the input word.
Dependencies are inferred from the claim text.