Abstract: Aspects of the present disclosure provide techniques for automated data classification through machine learning. Embodiments include determining, by a machine learning model, character-level embeddings of a plurality of characters from a text string. Embodiments include processing, by the machine learning model, the character-level embeddings through one or more bi-directional long short term memory (LSTM) layers. Embodiments include outputting, by the machine learning model based on the processing, a predicted label for the text string indicating a classification of the text string. Embodiments include performing, by a computing application, one or more actions based on the text string and the predicted label.

Abstract: Aspects of the present disclosure provide techniques for automated data classification error correction through machine learning. Embodiments include receiving a set of predicted labels corresponding to a set of consecutive text strings that appear in a particular order in a document, including: a first text string corresponding to a first predicted label; a second text string that follows the first text string in the particular order and corresponds to a second predicted label; and a third text string that follows the second text string in the particular order and corresponds to a third predicted label. Embodiments include providing inputs to a machine learning model based on: the third text string; the second text string; the second predicted label; and the first predicted label. Embodiments include determining a corrected third label for the third text string based on an output provided by the machine learning model in response to the inputs.

Abstract: Aspects of the present disclosure provide techniques for automated data classification error correction through machine learning. Embodiments include receiving a set of predicted labels corresponding to a set of consecutive text strings that appear in a particular order in a document, including: a first text string corresponding to a first predicted label; a second text string that follows the first text string in the particular order and corresponds to a second predicted label; and a third text string that follows the second text string in the particular order and corresponds to a third predicted label. Embodiments include providing inputs to a machine learning model based on: the third text string; the second text string; the second predicted label; and the first predicted label. Embodiments include determining a corrected third label for the third text string based on an output provided by the machine learning model in response to the inputs.

Abstract: A method for summarizing text is disclosed. The method can include a step of generating a connected network graph based on multiple portions of the text, wherein each portion of the text is a node of the network graph. The method can include a step of determining a similarity score of the multiple nodes of the network graph, wherein the similarity score of each node is based on its similarity with other nodes of the network graph. The method can include a step of measuring a centrality of each node of the network graph using graph centrality that is based on the similarity score and ranking the nodes based on the measured centrality. The method can include a step of generating a summary of the text by using one or more top ranked nodes.

Abstract: A rule having text is pre-processed by replacing terms with dummy tokens. A first machine learning model (MLM) uses the dummy tokens to generate a dependency graph with nodes related by edges tagged with dependency tags. A second MLM uses the dependency graph to generate a canonical version with node labels. The node labels are sorted into a lexicographic order to form a document. A third MLM uses the document to generate a machine readable vector (MRV) that embeds the document as a sequence of numbers representative of a structure of the rule. The MRV is compared to additional MRVs corresponding to additional rules for which computer useable program code blocks have been generated. A set of MRVs is identified that match the MRV within a range. The set of MRVs correspond to a set of rules from the additional rules. The set of rules is displayed to a user.

Abstract: A server may receive an inquiry associated with an interaction between a customer and a customer support agent from a device associated with a customer support agent; enter the inquiry as an input to a contextual bandit model; select, using the contextual bandit model, a collection of articles from a plurality of pre-defined collections of articles based on the inquiry; cause, in response to the contextual bandit model selecting the collection of articles, at least one search result to be displayed on a user interface of the device associated with the customer support agent, wherein a search result includes at least a portion of at least one article of the collection of articles; cause text within the at least one search result to be highlighted; receive feedback on the collection of articles from the customer support agent; and update the contextual bandit model based on the feedback.

Abstract: A rule having text is pre-processed by replacing terms with dummy tokens. A first machine learning model (MLM) uses the dummy tokens to generate a dependency graph with nodes related by edges tagged with dependency tags. A second MLM uses the dependency graph to generate a canonical version with node labels. The node labels are sorted into a lexicographic order to form a document. A third MLM uses the document to generate a machine readable vector (MRV) that embeds the document as a sequence of numbers representative of a structure of the rule. The MRV is compared to additional MRVs corresponding to additional rules for which computer useable program code blocks have been generated. A set of MRVs is identified that match the MRV within a range. The set of MRVs correspond to a set of rules from the additional rules. The set of rules is displayed to a user.