1. A method of determining and discerning items with multiple meanings in a sequence of items, the method comprising: producing a distributed representation for each item of the sequence of items including a word vector and a context vector; partitioning the sequence of items into classes; for an item using a representative word vector of each class, calculating a cosine distance between the word vector of said item and the class representative vector; and producing a new sequence of items by modifying the distributed representation in the producing by replacing each occurrence of an item in the distributed representation having a largest cosine distance between the word vector and the class representative vector calculated by the calculating; and wherein the producing the new sequence produces the new sequence by replacing each word occurrence in the input sequence of items by w_i, where i comprises the class index among closest classes that the cosine distance of the representative context vector of the class is closest to an average word vector for the items in a window around the item.
9. The method of claim 1 , further comprising producing for each item by calculating a fixed number of classes D with smallest said cosine distance.