Abstract: A computerized method for training a computer executed model for recognizing numerical quantities is provided. An input, at least one unit expression, is received by an input module. The input module may then search for numeric values and the unit expression in a text corpus, wherein, the text corpus comprises sets of words and frequency of occurrence of each of the sets. The input module may identify identified sets, wherein the identified sets may comprise a combination of a numeric value and the unit expression. A synthetic text generation module may then generate sentences from the text corpus by applying the identified sets as input. A training dataset may be generated by a labeling module by auto labelling features in the generated sentences based on the numeric value and the unit expression and further a training module may train the training model by providing input based on the training dataset.

Abstract: A computerized method for training a computer executed model for recognizing numerical quantities is provided. An input, atleast one unit expression, is received by an input module. The input module may then search for numeric values and the unit expression in a text corpus, wherein, the text corpus comprises sets of words and frequency of occurrence of each of the sets. The input module may identify identified sets, wherein the identified sets may comprise a combination of a numeric value and the unit expression. A synthetic text generation module may then generate sentences from the text corpus by applying the identified sets as input. A training dataset may be generated by a labeling module by auto labelling features in the generated sentences based on the numeric value and the unit expression and further a training module may train the training model by providing input based on the training dataset.

Abstract: The present invention discloses a method of creating word-level differential privacy with the hashing trick to protect confidentiality of a textual data, the method comprising: receiving a list of a plurality of hashes with a weight (or weights) associated with each of the plurality of hashes; Updating said list with new hashes that are within the range of allowable hash values but not included in said received list of hashes; Updating said list with a new weight to each of said plurality of hashes that are missing said weight; Fitting a probability distribution to said list of said weights of said plurality of hashes; and generating said new weights and said adjusted weights based on sampling of said probability distribution.

Abstract: A method for extracting of data contained in a fixed format electronic document is disclosed. The method is particularly applicable to extracting data from tables in electronic documents and includes reading, by a computer system, the electronic document as a computer image file; segmenting, by the computer system, the computer image file into document sections representative of distinct portions of data; applying a label to each distinct document section; and executing, by the computer system, an optical character recognition algorithm to convert the image file into computer-readable text, wherein segments of the converted text is associated with a respective label indicative of each distinct document section.

Abstract: A method for entity extraction within an electronic document including executing by a computer processor a conditional random field algorithm stored on a computer readable medium to generate a conditional random field model; the conditional random field algorithm having an input including one or more training text documents; executing by a computer processor an entity extraction algorithm stored on a computer readable medium to generate an entity extraction model; the entity extraction algorithm having an input including the same one or more training text documents input into the conditional random field algorithm; applying by a computer processor the conditional random field model to at least one electronic document; wherein application of the conditional random field model returns a list of passages in the at least one electronic document having an entity; applying by a computer processor the entity extraction model to the at least one electronic document; wherein application of the entity extraction model

Abstract: The methods proposed here deconstructs training sentences into a stream of features that represent both the sentences and tokens used by the text, their sequence and other ancillary features extracted using natural language processing. Then, we use a conditional random field where we represent the concept we are looking for as state A and the background (everything not concept A) as a state B. The model created by this training phase is then used to locate the concept as a sequence of sentences within a document. This has distinct advantages in accuracy and speed over methods that individually classify each sentence and then use a secondary method to group the classified sentences into passages. Furthermore while previous methods were based on searching for the occurrence of tokens only, the use of a wider set of features enables this method to locate relevant passages even though a different terminology is in use.