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.
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. 8. The method of claim 1 , wherein a first phase includes the producing the distributed representation, the partitioning, and the calculating the cosine distance, the method further comprising: in a second phase when the producing produces the new sequence: producing a modified sequence of items by replacing each item I occurrence in the sequence by an I_j occurrence where j is one of the D classes for item I; producing a new distributed representation for each item I_m of the modified sequence of items including a word vector and a context vector; and using said new distributed representation for determining and discerning items with multiple meanings.