Abstract: Systems, method, and computer-readable mediums for automated text classification, and particularly a mechanism for performing binary classification using only a set of positive labeled data as training data and having a large set of unlabeled data, where the algorithm can function without any information regarding the negative class. The disclosed classification systems and methods may use a text classification process which automatically classifies text based on the current positive training data available, but identifies additional words which can be added to the positive training data such that future iterations of the text classification can better identify the positive class of text.

Abstract: Examples provide a system for generating document-based responses to user provided queries. The response generation component creates a set of generated utterances based on text associated with a set of sentences in at least one document. Each utterance in the set of generated utterances is assigned an intent. A filter component identifies at least one utterance from the set of generated utterances having a same intent as a user-provided utterance to form a set of filtered utterances. A selection component identifies one or more utterance(s) from the set of filtered utterances having a shortest distance from the user-provided utterance. If more than one utterance is identified, a weighted summarization response is output to the user based on a predefined answer to each utterance in the selected set of utterances.

Abstract: Examples a system for customized travel planning management. The system extracts relevant travel-related data from travel-related provider contracts. The extracted relevant travel-related data and historical travel usage data are analyzed to calculate a market share for each provider. A booking manager identifies recommended travel-related provider pairs associated with a set of travel dates within a predetermined range of a user-selected preferred date of travel associated with a user-selected source-to-destination pair based on the calculated market share for each provider and user-provided travel preferences. The system books the user's selected accommodations provider and travel provided on a recommended booking date. An optimization component identifies recommended future negotiation terms associated with at least one provider in the set of travel-related providers based on the historical travel usage data, historical transaction data and historical contract compliance data.

Abstract: Examples provide a system for generating document-based responses to user provided queries. The response generation component creates a set of generated utterances based on text associated with a set of sentences in at least one document. Each utterance in the set of generated utterances is assigned an intent. A filter component identifies at least one utterance from the set of generated utterances having a same intent as a user-provided utterance to form a set of filtered utterances. A selection component identifies one or more utterance(s) from the set of filtered utterances having a shortest distance from the user-provided utterance. If more than one utterance is identified, a weighted summarization response is output to the user based on a predefined answer to each utterance in the selected set of utterances.

Abstract: Systems, method, and computer-readable mediums for automated text classification, and particularly a mechanism for performing binary classification using only a set of positive labeled data as training data and having a large set of unlabeled data, where the algorithm can function without any information regarding the negative class. The disclosed classification systems and methods may use a text classification process which automatically classifies text based on the current positive training data available, but identifies additional words which can be added to the positive training data such that future iterations of the text classification can better identify the positive class of text.