Abstract: The present disclosure provides a method for machine learning-based project risk prediction. The method includes training a first tree-based machine learning (ML) model using historical project delivery data to generate one or more risk-related predictions for project performance, determining a plurality of input features from the historical project delivery data, identifying a first value range for a first input feature, where contributions of the first input feature to one or more of the risk-related predictions, within the first value range, exhibit inconsistency that exceeds a defined threshold, training a second tree-based ML model using the historical project delivery data to identify a second input feature, where a combination of the first and second input features resolves the inconsistent contributions of the first input feature within the first value range, and generating a knowledge base table comprising the combination of the first and second input features.

Abstract: A computer-implemented process for updating an electronic document includes the following operations. Using a preprocessor, preprocessing is performed on the electronic document to generate a computer data structure. The computer data structure is evaluated using a word sense disambiguation (WSD) engine and a deep neural network to determine a context of a sentence within the electronic document. Based upon the context, a determination is made that a word within the sentence is a word of interest. The sentence is rewritten using a mitigation engine and a large language model to generate a revised sentence that does not include the word of interest. A determination is made that the revised sentence does not include any other word of interest; and the electronic document is updated to include the revised sentence.

Abstract: An embodiment for decomposing composite scanned documents. The embodiment may detect a target composite scanned document. The embodiment may extract, for sequential pages of the target composite scanned document, a series of document features. The embodiment may iteratively generate a series of sub-documents by iteratively adding a next page from the target composite scanned document to a series of one or more pages preceding the added next page. The embodiment may generate vector representations for each of the iteratively generated series of sub-documents, where each of the generated vector representations is based on the extracted series of document features. The embodiment may calculate similarity scores by comparing the generated vector representations with a knowledgebase of document vectors. The embodiment may cluster the sequential pages of the target composite scanned document based on the calculated similarity scores. The embodiment may output separate files including the clustered sequential pages.

Abstract: A computer-implemented process for updating an electronic document includes the following operations. Using a preprocessor, preprocessing is performed on the electronic document to generate a computer data structure. The computer data structure is evaluated using a word sense disambiguation (WSD) engine and a deep neural network to determine a context of a sentence within the electronic document. Based upon the context, a determination is made that a word within the sentence is a word of interest. The sentence is rewritten using a mitigation engine and a large language model to generate a revised sentence that does not include the word of interest. A determination is made that the revised sentence does not include any other word of interest; and the electronic document is updated to include the revised sentence.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to automatic sentence condition matching using natural language processing (NLP). The computer-implemented system can comprise a memory that can store computer-executable components and a processor that can execute the computer-executable components, wherein the computer-executable components can comprise an extraction module that can use a probabilistic relevance weighting model to retrieve a first sentence from a document by computing a normalized relevance score of the first sentence based on a relevance weighting score of a second sentence from a dictionary of query sentences. The computer-executable components can further comprise a resolution module that can use a set of NLP rules and a linguistic dictionary to automatically identify whether the first sentence and the second sentence have a same meaning based on the normalized relevance score being above a defined threshold.