Abstract: A method for removing text noise according to an embodiment of the present disclosure includes inspecting quality of the text, correcting the text based on a result of inspection; selecting a noise candidate based on each type of sentences included in the corrected text, wherein the noise candidate is selected for each sentence included in the text and removing at least some of the sentences included in the noise candidate based on the purpose of the text.

Abstract: A method for model customization according to an embodiment includes providing a user with prediction results of each of a plurality of pre-trained natural language processing models for a document subjected to analysis selected from a document set including a plurality of documents, acquiring user feedback on the prediction results from the user, generating a plurality of augmented documents from at least one of the plurality of documents based on data attributes of each of the plurality of documents and the user feedback; and retraining at least one of the plurality of natural language processing models, using training data including the plurality of augmented documents.

Abstract: A method for removing text noise according to an embodiment of the present disclosure includes inspecting quality of the text, correcting the text based on a result of inspection; selecting a noise candidate based on each type of sentences included in the corrected text, wherein the noise candidate is selected for each sentence included in the text and removing at least some of the sentences included in the noise candidate based on the purpose of the text.

Abstract: An apparatus for augmenting textual data according to an embodiment includes a data augmenter configured to generate augmented data by augmenting input textual data according to a data augmentation scheme decided based on a type of natural language processing task of the input textual data and a data classifier configured to classify the augmented data into a positive sample or a negative sample by determining whether or not the augmented data maintains label information of the input textual data based on one or more data classification criteria.

Abstract: A method for model customization according to an embodiment includes providing a user with prediction results of each of a plurality of pre-trained natural language processing models for a document subjected to analysis selected from a document set including a plurality of documents, acquiring user feedback on the prediction results from the user, generating a plurality of augmented documents from at least one of the plurality of documents based on data attributes of each of the plurality of documents and the user feedback; and retraining at least one of the plurality of natural language processing models, using training data including the plurality of augmented documents.

Abstract: A method and apparatus for detecting and managing faults, which can consider both causes from a device where a failure has occurred and causes from other devices as the causes of the failure, is provided. The method and apparatus may provide fault detect managing which divide analysis target data into a normal section and a faulty section and can thus perform fault detection and management using correlation coefficients that can distinctly show a failure.

Abstract: Methods for risk scoring in logistics system are provided, one of methods comprise, scoring events which may occur at an arbitrary node, scoring a risk at the arbitrary node based on information on the scored events and calculating a risk of a route including the arbitrary node using the scored risk at the arbitrary node.