Abstract: A system and method for data cleansing are disclosed. The method includes receiving by a data classifier, a data corpus comprising one or more words. The method further includes grouping the one or more words into a plurality of pre-defined categories and determining an overlap ratio between each pair of the plurality of pre-defined categories based on a comparison between each of the plurality of pre-defined categories. A normalized overlap ratio is computed for each of the plurality of pre-defined categories based on a combination of each overlap ratio. At least one of the plurality of pre-defined categories is determined based on a comparison between each of the normalized overlap ratio and an ideal overlap ratio. Finally, one or more common words from the selected at least one of the plurality of pre-defined categories is removed based on a frequency of occurrence of the one or more common words.

Abstract: A system and method for data cleansing are disclosed. The method includes receiving by a data classifier, a data corpus comprising one or more words. The method further includes grouping the one or more words into a plurality of pre-defined categories and determining an overlap ratio between each pair of the plurality of pre-defined categories based on a comparison between each of the plurality of pre-defined categories. A normalized overlap ratio is computed for each of the plurality of pre-defined categories based on a combination of each overlap ratio. At least one of the plurality of pre-defined categories is determined based on a comparison between each of the normalized overlap ratio and an ideal overlap ratio. Finally, one or more common words from the selected at least one of the plurality of pre-defined categories is removed based on a frequency of occurrence of the one or more common words.

Abstract: A data classifier computing device, method, and non-transitory computer readable medium for data classification are disclosed. The method includes receiving by a data classifier, a data corpus comprising one or more words. The method further includes comparing the data corpus with at least one pre-classified category of words to determine an overlap ratio between the data corpus and each of the at least one pre-classified category of words. The method further includes computing a confidence score of the data corpus for each of the at least one pre-classified category of words based on the overlap ratio and a predefined confidence score associated with the data corpus for each of the at least one pre-classified category of words. Finally, the method includes classifying the data corpus based on the confidence score into the at least one pre-classified category.

Abstract: In certain embodiments, the method may comprise, determining one or more features associated with each of one or more concepts from at least one sentence; determining at least one concept feature matrix based on the one or more features; determining one or more contribution factors among the one or more concepts, based on the at least one concept feature matrix; determining incremental synchronization data by storing current timestamp associated with the one or more concepts and comparing it with knowledge data; generating at least one new concept feature matrix based on the incremental synchronization data; determining one or more new contribution factors among the one or more concepts, based on the at least one new concept feature matrix; determining differential value between the one or more contribution factors and the one or more new contribution factors; and dynamically updating the knowledge data, in the repository system.

Abstract: In certain embodiments, the method may comprise, determining one or more features associated with each of one or more concepts from at least one sentence; determining at least one concept feature matrix based on the one or more features; determining one or more contribution factors among the one or more concepts, based on the at least one concept feature matrix; determining incremental synchronization data by storing current timestamp associated with the one or more concepts and comparing it with knowledge data; generating at least one new concept feature matrix based on the incremental synchronization data; determining one or more new contribution factors among the one or more concepts, based on the at least one new concept feature matrix; determining differential value between the one or more contribution factors and the one or more new contribution factors; and dynamically updating the knowledge data, in the repository system.