Abstract: Integrated circuit (IC) devices having dielectric material separating adjacent source and drain bodies. An IC device may include adjacent first and second transistor structures having adjacent source or drain bodies separated by a dielectric material on first and second sidewalls of a first of the source or drain bodies (but only on the sidewall of the second source or drain body adjacent the first source or drain body). An isolation structure of a second dielectric material may be on the first dielectric material, on the first and second source or drain bodies, and between first and second contact structures on the first and second source or drain bodies. The dielectric material on first and second sidewalls of the first source or drain body may be selectively deposited (e.g., conformally) before the second source or drain body is grown.

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: Systems and methods for automatically populating documents about special entities are disclosed herein. An example method is performed by one or more processors of a computing system. The example method may include receiving user data, extracting a list of entities associated with the user and a list of events that occurred between the user and the entities, transforming metadata for the events associated with entities of interest into vectorized embeddings, selectively classifying, using a binary classifier model, ones of the entities as special entities and ones of the events as special events for a set of documents, assigning, using a multi-class classifier model, one of a plurality of categories to each special event associated with each special entity, each of the categories mapping to a corresponding section within the set of documents, and populating, for each special entity, the corresponding sections within the set of documents based on the categories.

Abstract: The method may include generating, by a vector embedding model, a vector embedding of multiple terms in an input document to obtain multiple term encodings. The method may also include generating, by a cascading classifier model, a classification of the input document. Generating the classification includes iteratively: traversing a directed acyclic graph ordering multiple class groups, and while traversing the directed acyclic graph, classifying the input document into a first class of a current class group of the class groups using the term encodings, where classifying the input document into the first class uses at least one second class of at least one parent class group of the class groups, and where the classification includes the first class and the at least one second class. The method may furthermore include obtaining a set of target fields corresponding to the classification. The method may in addition include extracting a set of values from the input document matching the set of target fields.

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 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: 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.