DEEP LEARNING ENTITY MATCHING SYSTEM USING WEAK SUPERVISION

1 . A system comprising:

one or more processors; and

one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform functions comprising:

generating pairs of identities from a plurality of sources;

for each respective pair of identities of the pairs of identities:

determining a match probability for the respective pair of identities using a deep-learning transformer-based binary classification model; and

linking the respective pair of identities as nodes on a graph when the match probability meets a predetermined threshold, wherein a linkage between the nodes represents a match for the respective pair of identities;

generating, using a connected component algorithm, clusters each containing identities representing a respective user; and

generating a respective user profile for the respective user for each cluster.

2 . The system of claim 1 , wherein the computing instructions, when executed on the one or more processors, further cause the one or more processors to perform functions comprising:

generating a probabilistic set of labels for an unlabeled training dataset to output a labeled training dataset; and

training the deep-learning transformer-based binary classification model using the labeled training dataset.

3 . The system of claim 2 , wherein the probabilistic set of labels is generated using heuristic functions.

4 . The system of claim 2 , wherein generating the probabilistic set of labels uses a weak supervision model.

5 . The system of claim 1 , wherein determining the match probability comprises:

obtaining textual features for each identity of the respective pair of identities, wherein each of the textual features comprises unique string length distributions; and

generating a first sub-model based on the textual features.

6 . The system of claim 5 , wherein determining the match probability further comprises:

obtaining boolean features for each identity of the respective pair of identities, wherein the boolean features comprise external metadata and transaction history; and

generating a second sub-model based on the boolean features.

7 . The system of claim 6 , wherein generating the first sub-model comprises:

generating character-level encodings to convert the textual features into numeric representations;

sending the character-level encodings into a first embedding layer to generate a first embedding, wherein the first embedding layer is trained to remove sparsity from the character-level encodings;

sending the first embedding to an encoder block to generate final encodings, wherein the encoder block comprises a transformer using multi-head attention and a first fully connected layer using a Siamese architecture in which the encoder block is shared between two textual features;

calculating an absolute difference between the final encodings; and

passing each difference of each textual feature encoding into a second fully connected layer.

8 . The system of claim 7 , wherein generating the second sub-model comprises:

processing the boolean features using multiple fully connected layers.

9 . The system of claim 8 , wherein determining the match probability further comprises:

concatenating each output of the first sub-model and the second sub-model to generate a combined output;

passing the combined output, as concatenated, into a final fully connected layer; and

outputting the match probability.

10 . The system of claim 9 , wherein weights of deep-learning transformer-based binary classification model are tuned using a binary cross-entropy loss function.

11 . A method being implemented via execution of computing instruction configured to run on one or more processors and stored at one or more non-transitory computer-readable media, the method comprising:

generating pairs of identities from a plurality of sources;

for each respective pair of identities of the pairs of identities:

determining a match probability for the respective pair of identities using a deep-learning transformer-based binary classification model; and

linking the respective pair of identities as nodes on a graph when the match probability meets a predetermined threshold, wherein a linkage between the nodes represents a match for the respective pair of identities;

generating, using a connected component algorithm, clusters each containing identities representing a respective user; and

generating a respective user profile for the respective user for each cluster.

12 . The method of claim 11 , further comprising:

generating a probabilistic set of labels for an unlabeled training dataset to output a labeled training dataset; and

training the deep-learning transformer-based binary classification model using the labeled training dataset.

13 . The method of claim 12 , wherein the probabilistic set of labels is generated using heuristic functions.

14 . The method of claim 12 , wherein generating the probabilistic set of labels uses a weak supervision model.

15 . The method of claim 11 , wherein determining the match probability comprises:

obtaining textual features for each identity of the respective pair of identities, wherein each of the textual features comprises unique string length distributions; and

generating a first sub-model based on the textual features.

16 . The method of claim 15 , wherein determining the match probability further comprises:

obtaining boolean features for each identity of the respective pair of identities, wherein the boolean features comprise external metadata and transaction history; and

generating a second sub-model based on the boolean features.

17 . The method of claim 16 , wherein generating the first sub-model comprises:

generating character-level encodings to convert the textual features into numeric representations;

sending the character-level encodings into a first embedding layer to generate a first embedding, wherein the first embedding layer is trained to remove sparsity from the character-level encodings;

sending the first embedding to an encoder block to generate final encodings, wherein the encoder block comprises a transformer using multi-head attention and a first fully connected layer using a Siamese architecture in which the encoder block is shared between two textual features;

calculating an absolute difference between the final encodings; and

passing each difference of each textual feature encoding into a second fully connected layer.

18 . The method of claim 17 , wherein generating the second sub-model comprises:

processing the boolean features using multiple fully connected layers.

19 . The method of claim 18 , wherein determining the match probability further comprises:

concatenating each output of the first sub-model and the second sub-model to generate a combined output;

passing the combined output, as concatenated, into a final fully connected layer; and

outputting the match probability.

20 . The method of claim 19 , wherein weights of deep-learning transformer-based binary classification model are tuned using a binary cross-entropy loss function.