The present disclosure includes a computing device, a system, and method for performing a spreadsheet recalculation algorithm. In one embodiment, the computing device includes an electronic processor, and a memory coupled to the electronic processor. The memory includes Directed Acyclic Graph (DAG) data having a plurality of nodes, each node of the plurality of nodes having at least one of a constant value or one or more inputs, and program instructions. The program instructions, when executed by the electronic processor, cause the electronic processor to change the plurality of nodes, and update one or more affected nodes of the plurality of nodes based on the change to the plurality of nodes, the one or more affected nodes being less than all of the plurality of nodes.

A system to calculate a reconfigured confidence score is configured to receive a text, a plurality of labels, and a plurality of confidence scores from a plurality of models and assign a weightage to the inputs received from the plurality of models. The system is configured to select a first text with a first label and retrieve a second text, a third text, and a second label. The system is further configured to generate a first, second and third output confidence score for the first text, second text and third text, and corresponding labels. The system compares the plurality of output confidence scores and generates an output which comprises of the first text, the first label, and a final confidence score, wherein the final confidence score is one among the first, second and third output confidence scores.

A system allows users to perform analysis of objects processed by systems, for example, requests, traces, logs, and so on. The system allows users to use an object tagging language to categorize objects. Tagging rules specified using the object tagging language are executed to tag the objects processed. The system created a tagging metadata index based on the tagged objects. The tagging metadata index allows efficient execution of queries used for analyzing the objects. The system may be used for analyzing execution of systems, for example, to compare execution of replicas of a system to determine whether there are differences in the execution of different replicas.

A system allows users to perform analysis of objects processed by systems, for example, requests, traces, logs, and so on. The system allows users to use an object tagging language to categorize objects. Tagging rules specified using the object tagging language are executed to tag the objects processed. The system created a tagging metadata index based on the tagged objects. The tagging metadata index allows efficient execution of queries used for analyzing the objects. The system may be used for analyzing execution of systems, for example, to compare execution of replicas of a system to determine whether there are differences in the execution of different replicas.

One example method includes collecting annotated unstructured documents that each include a table with words whose respective column indices are known, using the documents to train a model to detect a table header in a given document, identifying, by the model, a region of a document that corresponds to a table header in a new document that is not part of the training data, using an algorithm to perform a segmentation process on the table header that identifies column boundaries in the table header, and to use the identified column boundaries to preliminarily assign a respective column index to each word in the table header. Finally, a graph neural network model is run on a graph that includes the words in the table, and running the graph neural network generates a refined prediction of a respective column index for each of the words in the table of the new document.

Systems and methods are provided herein for enabling a computing system to search and interact with service records containing natural language text to aid in the analysis of those records by: displaying in a user interface an image of a complex system; receiving, from the user interface, a high-level selection of criteria of the complex system; querying issue maps associated with individual natural language service records of a corpus of natural language service records based on the high-level selection, wherein the issue maps specify at least one term related to the complex system and a location on the complex system associated with the at least one term; and returning at least one issue map, wherein the at least one issue map returned specifies a term or location correlated to the criteria of the complex system indicated by the high-level selection.

The present application discloses a method and an apparatus for information query, and an electronic device, which relates to a field of deep learning (DL), natural language processing (NLP) and artificial intelligence (AI) technology. The method includes: receiving a query sentence, segmenting the query sentence to obtain word segments, and obtaining a dependency relationship between two word segments and part of speech of the word segments; obtaining a coding sequence of the query sentence according to the dependency relationship and the part of speech of the word segments; matching the coding sequence with a generalized template to obtain a core corpus of the query sentence, wherein the generalized template comprises part of speech to be extracted and a dependency relationship to be extracted; and obtaining a query result corresponding to the query sentence based on the core corpus. The application no longer relies on the accumulation of massive business scenario data to enhance a generalization ability, which ensures accurate and efficient information query, and improves the efficiency and reliability of the information query process. At the same time, it may support information query in different business scenarios, with strong expansion capability and high universality.

The disclosure provides a text classification model training method, a text classification method, an apparatus, an electronic device, and a computer-readable storage medium, and relates to artificial intelligence technology. The text classification model training method includes: performing machine translation on a plurality of first text samples in a first language to obtain a plurality of second text samples in a second language different from the first language; training a first text classification model for the second language based on a plurality of third text samples in the second language and corresponding class labels; performing confidence-based filtering on the plurality of second text samples by the trained first text classification model; and training a second text classification model for the second language based on the filtered second text samples.

A system, a method, and a computer program product for generation of keywords for a solution note for resolving an issue associated with a computing component. A dataset for training a keyword data model is received. The dataset includes a plurality of variables associated with one or more values. The keyword data model is configured for determination, as a function of one or more variables in the plurality of variables, of one or more keywords in a plurality of keywords associated with a computing solution in a plurality of computing solutions for resolving a problem with an operation of a computing component in a plurality of computing components. The keyword data model is trained using the received dataset and the keyword data model is applied to one or more variables in the received dataset to generate one or more keywords. One or more keywords associated with the computing solution is generated.

Examples herein involve detection of entities in unstructured data. Terms are extracted from unstructured data. Entities scores for the terms are calculated using information from a name probability source, a known entity database, and historical context information. The entity scores indicate a probability that the respective terms refer to entities. The presence of detected entities are indicated based on the entity scores.