Abstract: A system provides an end-to-end solution for invoice processing which includes reading files (such as pdfs and images), extracting key relevant information from the files, organizing the relevant information in a structured template as a key-value pair, and comparing files based on the similarities between different file fields to identify potential duplicate files.

Abstract: Disclosed are various embodiments for extracting transaction and user data from financial documents and formatting the data into a structured format to facilitate a real-time analysis of the extracted data. A user may submit an unstructured formatted financial document with a credit rating request, underwriting request, and/or other type of financial risk assessment request. Text components and a table component are identified according to a structural representation of the document. The text components are analyzed to identify and extract ownership data associated with the user that can be used to verify ownership of the provided document by the submitting user. The transaction data is identified and extracted in a structured format based at least in part on a table header location and detected column boundaries. The extracted transaction data is validated to ensure an accurate extraction of the transaction data.

Abstract: A system provides an end-to-end solution for invoice processing which includes reading files (such as pdfs and images), extracting key relevant information from the files, organizing the relevant information in a structured template as a key-value pair, and comparing files based on the similarities between different file fields to identify potential duplicate files.

Abstract: A system provides an end-to-end solution for invoice processing which includes reading invoices (both pdfs and images), extracting key relevant information from the face of invoices, organizing the relevant information in a structured template as a key-value pair, and comparing invoices based on the similarities between different invoice fields to identify potential duplicate invoices.

Abstract: A system provides an end-to-end solution for invoice processing which includes reading invoices (both pdfs and images), extracting key relevant information from the face of invoices, organizing the relevant information in a structured template as a key-value pair, and comparing invoices based on the similarities between different invoice fields to identify potential duplicate invoices.

Abstract: The disclosed embodiments illustrate method and system of processing data by a computing device for training a target domain classifier. The method includes extracting one or more first features and one or more second features from a first target instance, associated with a target domain. The method further includes predicting a first label for the received first target instance based on the one or more first features by utilizing a trained first classifier associated with a set of labeled source instances, wherein the predicted first label is assigned to the first target instance when a first score of the predicted first label exceeds a first pre-specified threshold. Further, the method includes updating a set of labeled target instances associated with the target domain based on the labeled first target instance, wherein the updated set of labeled target instances is utilized to train the target domain classifier.

Abstract: The disclosed embodiments illustrate a domain adaptation method for learning transferable feature representations from a source domain for a target domain. The method includes receiving real-time input data comprising labeled instances of the source domain and unlabeled instances of the target domain from a computing device. The method further includes determining source specific representation corresponding to the source domain and a common representation shared between the source domain and the target domain. Based on a positive contribution from the source specific representation and the common representation, the labeled instances of the source domain are classified. The method further includes training a generalized classifier based on a positive contribution from the common representation. The method further includes automatically performing text classification on the unlabeled instances of the target domain based on the trained generalized classifier.

Abstract: A system provides an end-to-end solution for invoice processing which includes reading invoices (both pdfs and images), extracting key relevant information from the face of invoices, organizing the relevant information in a structured template as a key-value pair, and comparing invoices based on the similarities between different invoice fields to identify potential duplicate invoices.

Abstract: The disclosed embodiments illustrate a domain adaptation method for learning transferable feature representations from a source domain for a target domain. The method includes receiving input data comprising a plurality of labeled instances of the source domain and a plurality of unlabeled instances of the target domain. The method includes learning common representation shared between the source domain and the target domain, based on the plurality of labeled instances of the source domain. The method includes labeling one or more unlabeled instances in the plurality of unlabeled instances of the target domain, based on the common representation. The method includes determining a target specific representation corresponding to the target domain.

Abstract: A system provides an end-to-end solution for invoice processing which includes reading invoices (both pdfs and images), extracting key relevant information from the face of invoices, organizing the relevant information in a structured template as a key-value pair, and comparing invoices based on the similarities between different invoice fields to identify potential duplicate invoices.

Abstract: The disclosed embodiments illustrate methods of data processing for text classification of a target domain. The method includes generating a plurality of clusters from a plurality of first text segments corresponding to a plurality of source domains, based on an association of the plurality of first text segments with a plurality of categories. The method further includes computing a similarity score of each of a plurality of second text segments corresponding to the target domain for each of the plurality of clusters. The method further includes identifying a pre-specified count of clusters from the plurality of clusters, based on the computed similarity score. Further, the method includes training a first classifier by utilizing first text segments in the identified pre-specified count of clusters, wherein the trained first classifier is utilized to automatically classify the plurality of second text segments into categories associated with the identified pre-specified count of clusters.

Abstract: The disclosed embodiments illustrate methods of data processing for training a target domain classifier to label text segments. The method includes identifying a set of common keywords with same label from a set of source keywords and a set of target keywords. The method includes training a first classifier, based on the set of common keywords, to label a first set of target text segments. The method includes training a second classifier based on at least a subset of the labeled first set of target text segments. The method includes training a third classifier, based on the first classifier and the second classifier, to label a second set of target text segments, wherein a subset of the labeled second set of target text segments is utilized for re-training the second classifier. The method further includes determining labels of another plurality of target text segments based on the re-trained second classifier.

Abstract: Methods and systems for training a conversation-classification model are disclosed. A first set of conversations in a source domain and a second set of conversation in a target domain are received. Each of the first set of conversations has an associated predetermined tag. One or more features are extracted from the first set of conversations and from the second set of conversations. Based on the similarity of content in the first set of conversations and the second set of conversations, a first weight is assigned to each conversation of the first set of conversations. Further, a second weight is assigned to the one or more features of the first set of conversations based on the similarity of the one or more features of the first set of conversations and of the second set of conversations. A conversation-classification model is trained based on the first weight and the second weight.

Abstract: The disclosed embodiments illustrate a domain adaptation method for learning transferable feature representations from a source domain for a target domain. The method includes receiving input data comprising a plurality of labeled instances of the source domain and a plurality of unlabeled instances of the target domain. The method includes learning common representation shared between the source domain and the target domain, based on the plurality of labeled instances of the source domain. The method includes labeling one or more unlabeled instances in the plurality of unlabeled instances of the target domain, based on the common representation. The method includes determining a target specific representation corresponding to the target domain.

Abstract: The disclosed embodiments illustrate a domain adaptation method for learning transferable feature representations from a source domain for a target domain. The method includes receiving real-time input data comprising labeled instances of the source domain and unlabeled instances of the target domain from a computing device. The method further includes determining source specific representation corresponding to the source domain and a common representation shared between the source domain and the target domain. Based on a positive contribution from the source specific representation and the common representation, the labeled instances of the source domain are classified. The method further includes training a generalized classifier based on a positive contribution from the common representation. The method further includes automatically performing text classification on the unlabeled instances of the target domain based on the trained generalized classifier.

Abstract: The disclosed embodiments illustrate methods of data processing for training a target domain classifier to label text segments. The method includes identifying a set of common keywords with same label from a set of source keywords and a set of target keywords. The method includes training a first classifier, based on the set of common keywords, to label a first set of target text segments. The method includes training a second classifier based on at least a subset of the labeled first set of target text segments. The method includes training a third classifier, based on the first classifier and the second classifier, to label a second set of target text segments, wherein a subset of the labeled second set of target text segments is utilized for re-training the second classifier. The method further includes determining labels of another plurality of target text segments based on the re-trained second classifier.

Abstract: The disclosed embodiments illustrate method and system of processing data by a computing device for training a target domain classifier. The method includes extracting one or more first features and one or more second features from a first target instance, associated with a target domain. The method further includes predicting a first label for the received first target instance based on the one or more first features by utilizing a trained first classifier associated with a set of labeled source instances, wherein the predicted first label is assigned to the first target instance when a first score of the predicted first label exceeds a first pre-specified threshold. Further, the method includes updating a set of labeled target instances associated with the target domain based on the labeled first target instance, wherein the updated set of labeled target instances is utilized to train the target domain classifier.

Abstract: The disclosed embodiments illustrate methods of data processing for text classification of a target domain. The method includes generating a plurality of clusters from a plurality of first text segments corresponding to a plurality of source domains, based on an association of the plurality of first text segments with a plurality of categories. The method further includes computing a similarity score of each of a plurality of second text segments corresponding to the target domain for each of the plurality of clusters. The method further includes identifying a pre-specified count of clusters from the plurality of clusters, based on the computed similarity score. Further, the method includes training a first classifier by utilizing first text segments in the identified pre-specified count of clusters, wherein the trained first classifier is utilized to automatically classify the plurality of second text segments into categories associated with the identified pre-specified count of clusters.

Abstract: Methods and systems for training a conversation-classification model are disclosed. A first set of conversations in a source domain and a second set of conversation in a target domain are received. Each of the first set of conversations has an associated predetermined tag. One or more features are extracted from the first set of conversations and from the second set of conversations. Based on the similarity of content in the first set of conversations and the second set of conversations, a first weight is assigned to each conversation of the first set of conversations. Further, a second weight is assigned to the one or more features of the first set of conversations based on the similarity of the one or more features of the first set of conversations and of the second set of conversations. A conversation-classification model is trained based on the first weight and the second weight.

Abstract: An adaptation method includes using a first classifier trained on projected representations of labeled objects from a first domain to predict pseudo-labels for unlabeled objects in a second domain, based on their projected representations. A classifier ensemble is iteratively learned. The ensemble includes a weighted combination of the first classifier and a second classifier. This includes training the second classifier on the original representations of the unlabeled objects for which a confidence for respective pseudo-labels exceeds a threshold. A classifier ensemble is constructed as a weighted combination of the first classifier and the second classifier. Pseudo-labels are predicted for the remaining original representations of the unlabeled objects with the classifier ensemble and weights of the first and second classifiers in the classifier ensemble are adjusted. As the iterations proceed, the unlabeled objects progressively receive pseudo-labels which can be used for retraining the second classifier.