Abstract: An approach is provided for ranking candidate answers to a natural language question. First contextual information about a user of a mobile device is identified. A prioritization of definitions of terms is determined. Based on the prioritization, a lexicon of the terms is generated. Using mobile-based time series manipulation and pattern recognition and based on historical usage of the mobile device, a location of the user, an environment of the user, and a bodily function of the user, second contextual information is forecasted. Based on a word sense disambiguation of the terms in the lexicon and an adjustment of the prioritization, candidate answers are modified and then ranked. The highest ranked candidate answer is more likely to be a correct answer to the natural language question than the other candidate answers.

Abstract: An approach is provided for ranking candidate answers to a natural language question. First contextual information about a user of a mobile device is identified. A prioritization of definitions of terms is determined. Based on the prioritization, a lexicon of the terms is generated. Using mobile-based time series manipulation and pattern recognition and based on historical usage of the mobile device, a location of the user, an environment of the user, and a bodily function of the user, second contextual information is forecasted. Based on a word sense disambiguation of the terms in the lexicon and an adjustment of the prioritization, candidate answers are modified and then ranked. The highest ranked candidate answer is more likely to be a correct answer to the natural language question than the other candidate answers.

Abstract: An approach is provided for ranking candidate answers to a natural language question. Candidate answers to a natural language question received from a mobile device are generated. First contextual information about a user of the mobile device is identified. A prioritization of definitions of terms is determined. Based on the prioritization, a lexicon of the terms is generated. Using mobile-based time series manipulation and pattern recognition and based on historical usage of the mobile device, a location of the user, an environment of the user, and a bodily function of the user, second contextual information is forecasted. Based on a word sense disambiguation of the terms in the lexicon and an adjustment of the prioritization, the candidate answers are modified and then ranked. The highest ranked candidate answer is more likely to be a correct answer to the natural language question than the other candidate answers.

Abstract: An approach is provided for ranking candidate answers to a natural language question. Candidate answers to a natural language question received from a mobile device are generated. First contextual information about a user of the mobile device is identified. A prioritization of definitions of terms is determined. Based on the prioritization, a lexicon of the terms is generated. Using mobile-based time series manipulation and pattern recognition and based on historical usage of the mobile device, a location of the user, an environment of the user, and a bodily function of the user, second contextual information is forecasted. Based on a word sense disambiguation of the terms in the lexicon and an adjustment of the prioritization, the candidate answers are modified and then ranked. The highest ranked candidate answer is more likely to be a correct answer to the natural language question than the other candidate answers.

Abstract: An approach is provided for ranking candidate answers to a natural language question. A natural language question is received from a user of a mobile device. Candidate answers to the received natural language question are generated. A lexicon is generated based on contextual information of the user determined by the mobile device. Contextual information of the user is forecasted based on usage of the mobile device. Based in part on the lexicon and the forecasted contextual information, the candidate answers to the natural language question are ranked.

Abstract: An approach is provided for ranking candidate answers to a natural language question. A natural language question is received from a user of a mobile device. Candidate answers to the received natural language question are generated. A lexicon is generated based on contextual information of the user determined by the mobile device. Contextual information of the user is forecasted based on usage of the mobile device. Based in part on the lexicon and the forecasted contextual information, the candidate answers to the natural language question are ranked.