Abstract: A method of using open domain information for understanding a context of temporal relation information is implemented as a computer program and performed using a computing device. Unnecessary elements is removed by data pre-processing from an input text in a natural language, and then linguistic characteristics of the pre-processed input text are analyzed to generate a linguistic analysis result in a structure form. Candidates for temporal relation information included in the input text are generated by analyzing temporal information and open domain information included in the input text using the linguistic analysis result, then validity of the candidates is verified to generate verified temporal relation information.

Abstract: Disclosed are a terminal, a server and event suggesting methods thereof, the event suggesting method using word embedding, the method including: determining user preference information about a predetermined object by assigning values to a plurality of reference items with respect to user input data corresponding to the object; generating user intent information, which includes values based on the plurality of reference items, based on at least one piece of review data corresponding to a plurality of events for recommendation objects, and accessing a database built up by adding the generated user intent information to the plurality of events; and suggesting an event following the object among the plurality of events based on a similarity between the user preference information about the object and the user intent information of each event included in the database.

Abstract: Disclosed are a terminal, a server and event suggesting methods thereof, the event suggesting method using word embedding, the method including: determining user preference information about a predetermined object by assigning values to a plurality of reference items with respect to user input data corresponding to the object; generating user intent information, which includes values based on the plurality of reference items, based on at least one piece of review data corresponding to a plurality of events for recommendation objects, and accessing a database built up by adding the generated user intent information to the plurality of events; and suggesting an event following the object among the plurality of events based on a similarity between the user preference information about the object and the user intent information of each event included in the database.