An electronic device for supporting a personalization service, the electronic device comprising: a storage storing a user-based dictionary; an inputter configured to receive an input of a sentence comprising a user-specific word not learned by a neural network-based language model and at least one word learned by the neural network-based language model where a vector space of the neural network-based language model represents a concept category space of at least one word; and a processor configured to: identify a concept category of the user-specific word based on semantic information of the input sentence by projecting the user-specific word into the concept category vector space of at least one word stored in the neural network-based language model recommended as at least one automatic completion word for an input sentence comprising words other than the user-specific word and determining the concept category of the user-specific word as the concept category vector space of the at least one automatic completion word if a calculated similarity between a vector of the user-specific word and a vector of the at least one automatic completion word is greater than or equal to a similarity threshold, add the user-specific word in the determined concept category of the user-based dictionary to update the user-based dictionary, and provide the user-specific word as an automatic completion recommendation word that may be input in the future when text corresponding to the semantic information is input. a storage storing a user-based dictionary; an inputter configured to receive an input of a sentence comprising a user-specific word not learned by a neural network-based language model and at least one word learned by the neural network-based language model where a vector space of the neural network-based language model represents a concept category space of at least one word; and a processor configured to: identify a concept category of the user-specific word based on semantic information of the input sentence by projecting the user-specific word into the concept category vector space of at least one word stored in the neural network-based language model recommended as at least one automatic completion word for an input sentence comprising words other than the user-specific word and determining the concept category of the user-specific word as the concept category vector space of the at least one automatic completion word if a calculated similarity between a vector of the user-specific word and a vector of the at least one automatic completion word is greater than or equal to a similarity threshold, add the user-specific word in the determined concept category of the user-based dictionary to update the user-based dictionary, and provide the user-specific word as an automatic completion recommendation word that may be input in the future when text corresponding to the semantic information is input. identify a concept category of the user-specific word based on semantic information of the input sentence by projecting the user-specific word into the concept category vector space of at least one word stored in the neural network-based language model recommended as at least one automatic completion word for an input sentence comprising words other than the user-specific word and determining the concept category of the user-specific word as the concept category vector space of the at least one automatic completion word if a calculated similarity between a vector of the user-specific word and a vector of the at least one automatic completion word is greater than or equal to a similarity threshold, add the user-specific word in the determined concept category of the user-based dictionary to update the user-based dictionary, and provide the user-specific word as an automatic completion recommendation word that may be input in the future when text corresponding to the semantic information is input.