Abstract: Systems and methods are disclosed for automatically extracting keys and corresponding values in any type of source document. Extracting desired words from the tokens in any type of document is based on a uniform approach to represent the source document. This uniform representation encodes features of the desired tokens along with the neighborhood information so that values associated with a given key can be extracted. The disclosed technique learns the representation of tokens independent of source document type and the learned representation is then used to determine relationships between multiple tokens. The neighborhood information and position information are used to determine various relationships between keys and values.

Abstract: Systems and methods are disclosed for automatically extracting keys and corresponding values in any type of source document. Extracting desired words from the tokens in any type of document is based on a uniform approach to represent the source document. This uniform representation encodes features of the desired tokens along with the neighborhood information so that values associated with a given key can be extracted. The disclosed technique learns the representation of tokens independent of source document type and the learned representation is then used to determine relationships between multiple tokens. The neighborhood information and position information are used to determine various relationships between keys and values.

Abstract: Methods, systems, and computer programs are presented for determining when to recommend posting in a group and joining a group. One method includes clustering posts by associating a topic identifier with each post based on the post text, and mapping each of the groups to one of the topic identifiers based on topics associated with the posts. A topic-to-group table, mapping each of the topic identifiers to one or more of the groups, is created, and a post classifier model is trained with the posts text and the topic identifiers. When an additional post is entered, the model determines a topic identifier for the additional post based on text of the additional post, and a group recommendation is determined for posting the additional post based on the topic identifier for the additional post and the table. The group recommendation is presented for posting the additional post in the recommended group.

Abstract: Methods, systems, and computer programs are presented for generating a question for a group and prompting the user to post that question. One method includes determining, for one or more groups of a user network, at least one group skill associated with the groups. A prompt is generated based on one or more user skills of a first user, including inserting at least one user skill into a prompt template to generate the prompt, where the prompt template includes a request to generate questions for the respective group. The prompt is provided as input into a generative artificial intelligence (GAI) model, a question is selected from the output of the GAI model, and a group is selected for based on the user skill inserted into the prompt template and the group skills associated with the groups. The selected question and the selected group are presented on a user interface.

Abstract: The present disclosure relates to a system and method to extract information from unstructured image documents. The extraction technique is content-driven and not dependent on the layout of a particular image document type. The disclosed method breaks down an image document into smaller images using the text cluster detection algorithm. The smaller images are converted into text samples using optical character recognition (OCR). Each of the text samples is fed to a trained machine learning model. The model classifies each text sample into one of a plurality of pre-determined field types. The desired value extraction problem may be converted into a question-answering problem using a pre-trained model. A fixed question is formed on the basis of the classified field type. The output of the question-answering model may be passed through a rule-based post-processing step to obtain the final answer.

Abstract: The present disclosure relates to a system and method to extract information from unstructured image documents. The extraction technique is content-driven and not dependent on the layout of a particular image document type. The disclosed method breaks down an image document into smaller images using the text cluster detection algorithm. The smaller images are converted into text samples using optical character recognition (OCR). Each of the text samples is fed to a trained machine learning model. The model classifies each text sample into one of a plurality of pre-determined field types. The desired value extraction problem may be converted into a question-answering problem using a pre-trained model. A fixed question is formed on the basis of the classified field type. The output of the question-answering model may be passed through a rule-based post-processing step to obtain the final answer.

Abstract: The present disclosure relates to a system and method to extract information from unstructured image documents. The extraction technique is content-driven and not dependent on the layout of a particular image document type. The disclosed method breaks down an image document into smaller images using the text cluster detection algorithm. The smaller images are converted into text samples using optical character recognition (OCR). Each of the text samples is fed to a trained machine learning model. The model classifies each text sample into one of a plurality of pre-determined field types. The desired value extraction problem may be converted into a question-answering problem using a pre-trained model. A fixed question is formed on the basis of the classified field type. The output of the question-answering model may be passed through a rule-based post-processing step to obtain the final answer.

Abstract: The present disclosure relates to systems and methods to extract customized keywords and their corresponding values occurring in a given natural language text. The desired keyword or keywords may occur in different forms, synonyms, abbreviations, and spellings. The disclosed automatic extraction method captures the meaning and context of the desired keywords by transforming the extraction problem into a question answering problem together with capturing the context to narrow down the answer to a unique value for a given keyword. A trained model on an existing corpus of text is used to get a value as an answer to the question phrased using the keyword. When the answer is ambiguous, a context model that uses conditional random field (CRF) is used to provide a most likely value.