Abstract: Systems and methods are provided for solving mathematical word problems using large language models.

Abstract: A method including receiving a natural language query from a user interface of a chatbot. The method also includes generating an input vector by performing vectorization on the natural language query. The method also includes inputting the input vector to a shallow-deep classifier. The shallow-deep learning classifier includes a classification machine learning model programmed to classify the input vector as being one of a shallow machine learning classification problem and a deep machine learning classification problem. The method also includes outputting, by the shallow-deep classifier, an output label. The output label includes one of the shallow machine learning classification problem and the deep machine learning classification problem.

Abstract: A system for account mapping includes functionality for obtaining more than one labeled accounts labeled by more than one accountant; pre-processing more than one labeled accounts using natural language processing, using the more than one pre-processed labeled accounts to train an account mapping model that performs multinomial classification; receiving an account name from an accounting application where the account name includes a text label for an account included in a chart of accounts; generating an account mapping by applying the account mapping model to the account name, where the account mapping includes a type of the account, a sub-type of the account, a code, and a series associated with an accounting form; returning the account mapping to the accounting application through an Application Programming Interface (API); and receiving a corrected account mapping from an accountant and using the corrected account mapping as a new text label to incrementally update the account mapping model.

Abstract: Processing compliance documents based on an artificial intelligence (AI) model is described herein. A system is configured to obtain a compliance document and obtain seed data associated with the compliance document. The seed data includes a plurality of sample text inputs and a plurality of sample computer readable operations associated with the plurality of sample text inputs. The system is also configured to parse text in the compliance document into one or more text segments, provide the one or more text segments and the seed data to the AI model, and obtain, from the AI model, one or more computer readable operations associated with the one or more text segments. The one or more computer readable operations are generated by the AI model based on few-shot learning using the seed data. The system is also configured to store the one or more computer readable operations for completing the compliance document.

Abstract: A method may include acquiring, from an initial document having a document type, initial document elements and initial attributes, deriving initial features for the initial document elements using the initial attributes, detecting initial form components using the initial features, clustering the initial form components into initial line objects of an initial structured representation by applying an unsupervised machine learning model to the geometric attributes of the initial document elements, acquiring, from a next document having the document type, next document elements and next attributes describing the next document elements, deriving next features for the next document elements using the next attributes, detecting next form components using the next features, determining that the initial form components and the next form components are different, clustering the next form components into next line objects of a next structured representation, and replacing the initial structured representation with the

Abstract: This disclosure relates to automatic text information extraction from electronic documents. An example system is configured to perform operations including obtaining text and one or more document features of an electronic document, clustering the text into one or more groups based on the one or more document features, and identifying one or more text strings from the text in one or more groups as one or more keys. Identifying the one or more text strings is based on the clustering. The system is also configured to perform operations including generating one or more key/value pairs. Generating one or more key/value pairs includes associating one or more values to the one or more keys (with a value including text outside of the one or more identified text strings). The system is further configured to output the one or more key/value pairs.

Abstract: Generating a difference between a first and second plurality of lines of text in structured machine-readable format may include determining, by at least one processor, a line of the second plurality of lines that constitutes a best match for a line of the first plurality of lines. The line of the first plurality of lines and its respective best match may be associated with a similarity score. The at least one processor may compare the similarity score to a threshold value. In response to determining that the similarity score is greater than or equal to the threshold value, the at least one processor may compute, the textual difference between the line of the first plurality of lines and its best match. In response to computing the textual difference, the at least one processor may analyze the textual difference to identify a non-meaningful change.

Abstract: A method may include acquiring, from an initial document having a document type, initial document elements and initial attributes, deriving initial features for the initial document elements using the initial attributes, detecting initial form components using the initial features, clustering the initial form components into initial line objects of an initial structured representation by applying an unsupervised machine learning model to the geometric attributes of the initial document elements, acquiring, from a next document having the document type, next document elements and next attributes describing the next document elements, deriving next features for the next document elements using the next attributes, detecting next form components using the next features, determining that the initial form components and the next form components are different, clustering the next form components into next line objects of a next structured representation, and replacing the initial structured representation with the

Abstract: A method may include acquiring, from a document, document elements and attributes describing the document elements. One or more of the attributes may be geometric attributes describing a placement of the corresponding document element within the document. The method may further include deriving features for the document elements using the attributes, detecting form components using the features, clustering the form components into line objects of a structured representation by applying an unsupervised machine learning model to the geometric attributes of the document elements, and populating a compliance form using the structured representation.

Abstract: A method may include acquiring, from a document, document elements and attributes describing the document elements. One or more of the attributes may be geometric attributes describing a placement of the corresponding document element within the document. The method may further include deriving features for the document elements using the attributes, detecting form components using the features, clustering the form components into line objects of a structured representation by applying an unsupervised machine learning model to the geometric attributes of the document elements, and populating a compliance form using the structured representation.

Abstract: A system for account mapping includes functionality for obtaining more than one labeled accounts labeled by more than one accountant; pre-processing more than one labeled accounts using natural language processing, using the more than one pre-processed labeled accounts to train an account mapping model that performs multinomial classification; receiving an account name from an accounting application where the account name includes a text label for an account included in a chart of accounts; generating an account mapping by applying the account mapping model to the account name, where the account mapping includes a type of the account, a sub-type of the account, a code, and a series associated with an accounting form; returning the account mapping to the accounting application through an Application Programming Interface (API); and receiving a corrected account mapping from an accountant and using the corrected account mapping as a new text label to incrementally update the account mapping model.

Abstract: A system for account mapping includes functionality for obtaining more than one labeled accounts labeled by more than one accountant; pre-processing more than one labeled accounts using natural language processing, using the more than one pre-processed labeled accounts to train an account mapping model that performs multinomial classification; receiving an account name from an accounting application where the account name includes a text label for an account included in a chart of accounts; generating an account mapping by applying the account mapping model to the account name, where the account mapping includes a type of the account, a sub-type of the account, a code, and a series associated with an accounting form; returning the account mapping to the accounting application through an Application Programming Interface (API); and receiving a corrected account mapping from an accountant and using the corrected account mapping as a new text label to incrementally update the account mapping model.

Abstract: The present invention provides novel Quercetin derivatives of formula (I) and pharmaceutically acceptable salts, hydrates, and solvates thereof, wherein R1 is hydrogen, benzyl or substituted benzyl; R2 is hydrogen, benzyl or substituted benzyl, linear or branched (C1-C6) alkyl, substituted alkyl, aryl, substituted aryl, heterocycle and substituted heterocycle, useful for treatment of various disorders including cancer, multi-drug resistant cancers, viral infections etc. The invention also provides a process for the preparation of compounds of formula (I) and pharmaceutical compositions comprising the same.

Abstract: The invention relates to pharmaceutical compositions comprising 5-Methoxytryptamine or a salt thereof for the prevention and/or treatment of mammalian cardiac tissue damage. 5-Methoxytryptamine and the salts thereof act as free radical scavengers in the prevention and/or treatment of mammalian cardiac tissue damage mediated by free oxygen radicals.

Abstract: The present invention relates to a combination of peptides that may be used for treatment of cancer. The peptide combination competes for the binding of specific neuropeptides at the plasma membrane and thereby alters the levels of key intracellular molecules implicated in cell proliferation, resulting in a broad spectrum of anticancer activity. The invention also relates to pharmaceutical compositions containing a combination of such peptide analogs.

Abstract: This invention relates to an orally effective herbal extract-based composition having broad-spectrum anticancer activity, more specifically a method of treating, inhibiting and/or preventing malignant tumors of the colon, intestine, stomach, breast, melanoma, glioblastoma, lung, cervix, ovary, prostate, oral cavity, larynx, liver, pancreas, kidney, bladder, endothelial cells, leukemia and myeloma using a herbal extract of Zizyphus, rich in betulinic acid. An advantage of the extract is that the betulinic acid has low systemic toxicity. The extract inhibits Protein Kinase C activity of cancer cells and induces apoptosis.

Abstract: The invention relates to pharmaceutical compositions comprising 5-Methoxytryptamine or a salt thereof for the prevention and/or treatment of mammalian cardiac tissue damage. 5-Methoxytryptamine and the salts thereof act as free radical scavengers in the prevention and/or treatment of mammalian cardiac tissue damage mediated by free oxygen radicals.