Abstract: The present disclosure is directed towards systems and methods for detecting deviations between documents and portions thereof, extracting information from text and detecting deviations between obligations. Information is extracted by identifying defined terms and their definitions in input text as well as by identifying portions of different input texts relevant to a point of interest and detecting deviations in those portions between the different input texts.

Abstract: Computer systems and computer implemented methods for training a machine learning model are provided that includes: selecting seed data from an unlabeled dataset; labeling the seed data and storing the labeled seed data in a data store; training the machine learning model in an initial iteration using the labeled seed data, where the machine learning model is trained to select a next subset of the unlabeled dataset; selecting a next subset of the unlabeled dataset; computing difficulty scores for at least the next subset of the unlabeled dataset; labeling the next subset of the unlabeled data; and training the machine learning model in a second iteration using the labeled next subset of the unlabeled dataset. The machine learning model is generally trained to select the next subset of the unlabeled dataset for a subsequent training iteration by presenting the labeled next subset of the unlabeled dataset in an order sorted based on the difficulty scores.

Abstract: In embodiments, a sentiment analyzer identifies a first event and accesses a first set of messages. The sentiment analyzer associates the first set of messages with the first event and analyzes the messages to identify a set of sentiment features. The set of sentiment features is used to analyze a second set of messages to form a prediction associated with a second event. The prediction may be used to facilitate an event-related service.

Abstract: The present disclosure is directed towards systems and methods for detecting deviations between documents and portions thereof, extracting information from text and detecting deviations between obligations. Deviations are detected by splitting sentences in two documents apart, matching sentences from the different documents and detecting deviations between the matched sentences.

Abstract: The present disclosure is directed towards systems and methods for detecting deviations between documents and portions thereof, extracting information from text and detecting deviations between obligations. Deviations between sentences containing obligations are detected by classifying the sentences, identifying components of the obligations and identifying differences between the obligation components.

Abstract: The present disclosure is directed towards systems and methods for detecting deviations between documents and portions thereof, extracting information from text and detecting deviations between obligations. Information is extracted by identifying defined terms and their definitions in input text as well as by identifying portions of different input texts relevant to a point of interest and detecting deviations in those portions between the different input texts.

Abstract: In embodiments, a message retriever accesses a plurality of messages and a message filter identifies, within the accessed messages, a set predictive messages. The risk/return-related messages are analyzed to identify associated return advisors. The return advisors are evaluated and ranked according to advisor scores, and risk/return-related decisions referenced in the messages are also identified, evaluated, and ranked. The ranked return advisors and decisions are used to facilitate assessment of future performance of an item or entity.

Abstract: A method includes analyzing a cluster of conceptually-related portions of text to develop a model and calculating a novelty measurement between a first identified conceptually-related portion of text and the model. The method further includes transmitting a second identified conceptually-related portion of text and a score associated with the novelty measurement from a server to an access device via a signal. Another method includes determining at least two corpora of conceptually-related portions of text. The method also includes calculating a common neighbors similarity measurement between the at least two corpora of conceptually-related portions of text and if the common neighbors similarity measurement exceeds a threshold, merging the at least two corpora of conceptually-related portions of text into a cluster or if the common neighbors similarity measurement does not exceed a threshold, maintaining a non-merge of the at least two corpora of conceptually-related portions of text.

Abstract: In embodiments, a message retriever accesses a plurality of messages and a message filter identifies, within the accessed messages, a set predictive messages. The risk/return-related messages are analyzed to identify associated return advisors. The return advisors are evaluated and ranked according to advisor scores, and risk/return-related decisions referenced in the messages are also identified, evaluated, and ranked. The ranked return advisors and decisions are used to facilitate assessment of future performance of an item or entity.

Abstract: In embodiments, a sentiment analyzer identifies a first event and accesses a first set of messages. The sentiment analyzer associates the first set of messages with the first event and analyzes the messages to identify a set of sentiment features. The set of sentiment features is used to analyze a second set of messages to form a prediction associated with a second event. The prediction may be used to facilitate an event-related service.

Abstract: A method includes analyzing a cluster of conceptually-related portions of text to develop a model and calculating a novelty measurement between a first identified conceptually-related portion of text and the model. The method further includes transmitting a second identified conceptually-related portion of text and a score associated with the novelty measurement from a server to an access device via a signal. Another method includes determining at least two corpora of conceptually-related portions of text. The method also includes calculating a common neighbors similarity measurement between the at least two corpora of conceptually-related portions of text and if the common neighbors similarity measurement exceeds a threshold, merging the at least two corpora of conceptually-related portions of text into a cluster or if the common neighbors similarity measurement does not exceed a threshold, maintaining a non-merge of the at least two corpora of conceptually-related portions of text.