1. A computer-implemented method comprising: receiving a term; determining by the computer an ambiguity score for the term, wherein the ambiguity score is on ratio of a probability of the term and at least first, second and third language models of a plurality of language models, and wherein the first language model is based on a medical corpus of documents and the second language model is based on a general news corpus of documents and the third language model is based on a legal corpus of documents, wherein the ambiguity score for the term is determined using the function: S t n = λ 1 ⁢ log ⁡ ( P ⁡ ( t n | M 2 ) ) log ⁡ ( P ⁡ ( t n | M 1 ) ) + λ 2 ⁢ log ⁡ ( P ⁡ ( t n | M 3 ) ) log ⁡ ( P ⁡ ( t n | M 1 ) ) where S t n is the ambiguity score for term t n ,λ 1 is a first constant, λ 2 is a second constant, P is a function of probability, M 1 is the first language model, M 2 is the second language model, and M 3 is a third language model; and outputting by the computer the ambiguity score for the term, wherein the ambiguity score for the term is outputted as ranked list, with each score associated with corresponding terms.