System and method for fast annotation of dataset with constraint propagation

A system for annotating an unlabeled dataset with constraint propagation, the system comprising:

• a processor;
• and
• a memory operatively connected to the processor via a communication interface, the memory storing computer readable instructions, when executed, causes the processor to:
• implement a platform, language, cloud, and database agnostic smart data annotation module (SDAM), by an smart data annotation device (SDAD) executed by at least one processor, the SDAD being hosted on a cloud based network environment, wherein SDAM includes a receiving module, a querying module, an annotating module, a generating module, propagating module, labeling module, implementing module, and a training module, wherein each module being called via a corresponding application programming interface, and wherein the SDAM is paired with a machine learning data model so that the SDAM can output examples which are most useful to train the machine learning data model;
• receive, by the machine learning data model, an unlabeled dataset by calling the receiving module;
• query, by utilizing a user interface, the machine learning data model to fetch the unlabeled dataset by calling the querying module;
• annotate, by calling the annotating module, the unlabeled dataset by labeling pairs of examples which are labeled with binary “YES” or “No” answers;
• generate, in response to annotating the unlabeled dataset, by calling the generating module, an annotated labeled dataset having annotated examples;
• propagate, by calling the propagating module, constraints based on a preconfigured rule in a manner such that a user can go back to previously annotated examples from the annotated labeled dataset;
• label, by calling the labeling module, the previously annotated examples correctly when the user determines that the previously annotated examples are mis-labeled;
• implement, by calling the implementing module, a caching algorithm to keep a dictionary of edges which are induced by an annotation in a manner such that when the user “undo” an annotated edge, the user can revert the edges that are induced by the edge;
• and
• automatically train, by calling the training module, the machine learning data model with correct labeling of the annotated examples.

A non-transitory computer readable medium configured to store instructions for annotating an unlabeled dataset with constraint propagation, the instructions, when executed, cause a processor to perform the following:

• implementing a platform, language, cloud, and database agnostic smart data annotation module (SDAM), by an smart data annotation device (SDAD) executed by at least one processor, the SDAD being hosted on a cloud based network environment, wherein SDAM includes a receiving module, a querying module, an annotating module, a generating module, propagating module, labeling module, implementing module, and a training module, wherein each module being called via a corresponding application programming interface, and wherein the SDAM is paired with a machine learning data model so that the SDAM can output examples which are most useful to train the machine learning data model;
• receiving, by the machine learning data model, an unlabeled dataset by calling the receiving module;
• querying, by utilizing a user interface, the machine learning data model to fetch the unlabeled dataset by calling the querying module;
• annotating, by calling the annotating module, the unlabeled dataset by labeling pairs of examples which are labeled with binary “YES” or “No” answers;
• generating, in response to annotating the unlabeled dataset, by calling the generating module, an annotated labeled dataset having annotated examples;
• propagating, by calling the propagating module, constraints based on a preconfigured rule in a manner such that a user can go back to previously annotated examples from the annotated labeled dataset;
• labeling, by calling the labeling module, the previously annotated examples correctly when the user determines that the previously annotated examples are mis-labeled;
• implementing, by calling the implementing module, a caching algorithm to keep a dictionary of edges which are induced by an annotation in a manner such that when the user “undo” an annotated edge, the user can revert the edges that are induced by the edge;
• and
• automatically training, by calling the training module, the machine learning data model with correct labeling of the annotated examples.