Oracle International Corporation

Xu Zhong

Mark Edward Johnson

Thanh Long Duong

Vishal Vishnoi

Srinivasa Phani Kumar Gadde

Vishank Bhatia

Yakupitiyage Don Thanuja Samodhye Dharmasiri

Sagar Gollamudi

Shashi Prasad Suravarapu

King-Hwa Lee

Budhaditya Saha

Ahmed Ataallah Ataallah Abobakr

Hongtao Yang

Shaoke Xu

Christopher Kennewick

A data corpus is partitioned into text strings for header classification. A group characteristic is computed for a text string, and whether the group characteristic satisfies a group characteristic criterion is determined. The text string may be disqualified from header classification if the group characteristic criterion is not satisfied, or one or more font characteristics may be determined for the text string if the group characteristic criterion is satisfied. A font characteristic that meets one or more prevalence criteria may be identified and evaluated to determine whether the font characteristic meets at least one font characteristic criterion. The text string may be disqualified from header classification if the font characteristic criterion is not satisfied, or if the font characteristic meets the font characteristic criterion, the text string is classified as a header, and tagged content is generated by applying a header tag to the text string.

Identifying and formatting headers for text content

Techniques are disclosed for rules-based techniques for extraction of question-and-answer pairs from digital documents. In an exemplary technique, a digital text document can be accessed by executing a document indicator. A document hierarchy can be generated. The document hierarchy can include at least one parent node and child nodes corresponding to the parent node. Each node of the child nodes can correspond to a text feature of the digital text document. At least a first child node of the child nodes can be determined that corresponds to a first text feature. The parent node of the first child node and a second child node of the parent node can be determined. The second child node can correspond to a second text feature related to the first text feature. A training data set can be generated.

Rule-based techniques for extraction of question and answer pairs from data

Deep learning techniques are disclosed for extraction of embedded data from documents. In an exemplary technique, a set of unstructured text data is received. One or more text groupings are generated by processing the set of unstructured text data. One or more text grouping embeddings are generated in a format for input to a machine learning model based on the one or more generated text groupings. One or more output predictions are generated by inputting the one or more text grouping embeddings into the machine learning model. Each output prediction of the one or more output predictions correspond to a predicted aspect of a text grouping of the one or more text groupings.

Deep learning techniques for extraction of embedded data from documents

Techniques for maintaining list-type text formatting when converting content from a source content format to a destination content format are disclosed. A system generates text content by applying text formatting tags to segments of characters obtained from a source electronic document. The system parses a static-display type source electronic document to obtain character data of the characters in the source document. The system analyzes the parsed data to identify text arranged in a list-type text format in the source document. The system generates text content in a destination content format different from the source format by applying tags to segments of the text content designating the segments items in a list.

Generating tagged content from a list in an electronic document

Techniques for extracting key information from a document using machine-learning models in a chatbot system is disclosed herein. In one particular aspect, a method is provided that includes receiving a set of data, which includes key fields, within a document at a data processing system that includes a table detection module, a key information extraction module, and a table extraction module. Text information and corresponding location data are extracted via optical character recognition. The table detection module detects whether one or more tables are present in the document and, if applicable, a location of each of the tables. The key information extraction module extracts text from the key fields. The table extraction module extracts each of the tables based on input from the optical character recognition and the table detection module. Extraction results include the text from the key fields and each of the tables can be output.

Extracting key information from document using trained machine-learning models

Techniques for generating formatting tags for textual content obtained from a source electronic document are disclosed. A system parses a digital file to obtain information about characters in an electronic document. The system applies tags to text generated based on the textual content of the electronic document by creating segments of textually-consecutive characters and applying corresponding text formatting style tags to the segments. The system further identifies segments of text overlapping bounding boxes in the electronic document. The system generates textual content including a segment of text and a corresponding hyperlink associated with the segment of text. The system further generates textual content by selectively applying line breaks from the source electronic document in the textual content.

Generating tagged content from text of an electronic document

Techniques for generating text content arranged in a consistent read order from a source document including text corresponding to different read orders are disclosed. A system parses a binary file representing an electronic document to identify characters and metadata associated with the characters. The system pre-sorts a character order of characters in each line of the electronic document to generate an ordered list of characters arranged according to the right-to-left reading order. The system performs a layout-mirroring operation to change a position of characters within the modified document relative to a right edge of the document and a left edge of the document. Subsequent to performing layout-mirroring, the system identifies native left-to-right reading-order text in-line with the native right-to-left reading-order text. The system flips the reading order of the native left-to-right read-order characters into the left-to-right reading order to be consistent with the native right-to-left read-order text.

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Xu Zhong