Identifying documents that contain potential code words using a machine learning model

A system comprising:

• one or more non-transitory computer-readable media comprising one or more computer-readable instructions;
• and
• one or more processors configured to execute the computer-readable instructions to cause the system to perform operations, the operations comprising:
• identifying a plurality of document corpuses from a plurality of documents;
• extracting a set of word embeddings from each of the plurality of document corpuses such that multiple sets of word embeddings are extracted;
• generating a plurality of vector spaces for the sets of word embeddings using a machine learning model, with each set of word embeddings having a unique vector space;
• selecting a first group of the plurality of vector spaces;
• grouping the plurality of vector spaces in pairs, such that each of the plurality of vector spaces not included in the first group is grouped with at least one of the vector spaces in the first group to form vector space pairs;
• comparing the word embeddings of the vector spaces that are paired;
• identifying a word embedding as a potential code word based on the comparisons of the word embeddings of the vector spaces that are paired;
• and
• identifying one or more documents that contain the potential code word.

A computer-implemented method for identifying documents that contain potential code words using a machine learning model, at least a portion of the method being performed by a computer system comprising one or more processors, the method comprising:

• extracting, by the computer system, a set of word embeddings from each of a plurality of document corpuses such that multiple sets of word embeddings are extracted;
• generating, by the computer system, a plurality of vector spaces for the sets of word embeddings using a machine learning model, with each set of word embeddings having a unique vector space;
• performing a vector rotation of one or more vector spaces of the plurality of vector spaces to improve alignment of the plurality of vector spaces;
• grouping, by the computer system, the plurality of vector spaces in pairs, such that a first vector space of the plurality of vector spaces is grouped with a second vector space of the plurality of vector spaces in a first pair and a third vector space of the plurality of vector spaces in a second pair;
• comparing, by the computer system, the word embeddings of the vector spaces that are paired;
• identifying, by the computer system, a word embedding as a potential code word based on the comparisons of the word embeddings of the vector spaces that are paired;
• and
• identifying, by the computer system, one or more documents that contain the potential code word.

A computer-implemented method for identifying documents that contain potential code words using a machine learning model, at least a portion of the method being performed by a computer system comprising one or more processors, the method comprising:

• extracting, by the computer system, a set of word embeddings from each of a plurality of document corpuses such that multiple sets of word embeddings are extracted;
• generating, by the computer system, a plurality of vector spaces for the sets of word embeddings using a machine learning model, with each set of word embeddings having a unique vector space;
• grouping, by the computer system, the plurality of vector spaces in pairs, such that a first vector space of the plurality of vector spaces is grouped with a second vector space of the plurality of vector spaces in a first pair and a third vector space of the plurality of vector spaces in a second pair;
• comparing, by the computer system, the word embeddings of the vector spaces that are paired;
• combining, based on the comparisons, a pair of vector spaces;
• grouping the combined vector spaces with another vector space of the plurality of vector spaces in a third pair;
• comparing, by the computer system, the word embeddings of the third pair of vector spaces;
• identifying, by the computer system, a word embedding as a potential code word based on the comparisons of the word embeddings of the vector spaces that are paired;
• and
• identifying, by the computer system, one or more documents that contain the potential code word.