8. A computer-implemented method of contextual text adaptation, the method performed by one or more hardware processors, comprising: receiving a corpus of documents in context of a target user; receiving a dictionary of words; receiving a dictionary of synonyms; generating a topic model algorithm based on the corpus of documents and the dictionary of words by machine learning, the topic model algorithm 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; and storing the first function and the second function of the topic model algorithm in a storage device, receiving an input document; determining 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; determining an aggregate probability indicating relevance of an input document word to the input document topics based on executing the second function; determining a synonym of the input document word based on the dictionary of synonyms; determining an aggregate probability for the synonym based on executing the second function; comparing 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, replacing the input document word with the synonym; and generating an output document comprising content of the input document with replaced word.
8. A computer-implemented method of contextual text adaptation, the method performed by one or more hardware processors, comprising: receiving a corpus of documents in context of a target user; receiving a dictionary of words; receiving a dictionary of synonyms; generating a topic model algorithm based on the corpus of documents and the dictionary of words by machine learning, the topic model algorithm 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; and storing the first function and the second function of the topic model algorithm in a storage device, receiving an input document; determining 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; determining an aggregate probability indicating relevance of an input document word to the input document topics based on executing the second function; determining a synonym of the input document word based on the dictionary of synonyms; determining an aggregate probability for the synonym based on executing the second function; comparing 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, replacing the input document word with the synonym; and generating an output document comprising content of the input document with replaced word. 10. The method of claim 8 , 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.