1. A system for contextual text adaptation, comprising: one or more hardware processors; a topic model algorithm executable on one or more of the hardware processors, the topic model algorithm generated by machine learning based on a corpus of documents at least related to context of a target user, the topic model comprising a first function that predicts probability distribution of a plurality of topics in a given document, and a second function that predicts probability of a given word occurring in a document associated with a given topic, one or more of the hardware processors operable to receive an input document, one or more of the hardware processors further operable to determine input document topics associated with the input document and a normalized weight associated with each of the input document topics by executing the first function, one or more of the hardware processors further operable to determine an aggregate probability indicating relevance of an input document word to the input document topics based on executing the second function, one or more of the hardware processors further operable to determine a synonym of the input document word based on a dictionary of synonyms, one or more of the hardware processors further operable to determine an aggregate probability for the synonym based on executing the second function, one or more of the hardware processors further operable to compare the aggregate probability for the synonym and the aggregate probability for the input document word, and responsive to determining that the aggregate probability for the synonym is greater than the aggregate probability for the input document word, one or more of the hardware processors further operable to replace the input document word with the synonym, one or more of the hardware processors further operable to generate an output document comprising content of the input document with replaced word.
1. A system for contextual text adaptation, comprising: one or more hardware processors; a topic model algorithm executable on one or more of the hardware processors, the topic model algorithm generated by machine learning based on a corpus of documents at least related to context of a target user, the topic model comprising a first function that predicts probability distribution of a plurality of topics in a given document, and a second function that predicts probability of a given word occurring in a document associated with a given topic, one or more of the hardware processors operable to receive an input document, one or more of the hardware processors further operable to determine input document topics associated with the input document and a normalized weight associated with each of the input document topics by executing the first function, one or more of the hardware processors further operable to determine an aggregate probability indicating relevance of an input document word to the input document topics based on executing the second function, one or more of the hardware processors further operable to determine a synonym of the input document word based on a dictionary of synonyms, one or more of the hardware processors further operable to determine an aggregate probability for the synonym based on executing the second function, one or more of the hardware processors further operable to compare the aggregate probability for the synonym and the aggregate probability for the input document word, and responsive to determining that the aggregate probability for the synonym is greater than the aggregate probability for the input document word, one or more of the hardware processors further operable to replace the input document word with the synonym, one or more of the hardware processors further operable to generate an output document comprising content of the input document with replaced word. 6. The system of claim 1 , wherein the aggregate probability for the input document word is determined as a sum of products of the probability that the input document word is associated with an input document topic and the normalized weight of the input document topic.