6. The computer-implemented method of claim 5 , wherein the distributional similarity score is based on a cosine similarity, a dot-product, a mutual information, a Jensen Shannon divergence, or a dice coefficient.
5. The computer-implemented method of claim 2 , further comprising determining the distributional similarity score between the first query and each second query comprising: for the first query and each respective second query: selecting, for each query term in the respective query, context terms from terms included in a third corpus, wherein each context term is selected based on a distance metric between the query term included in the respective query and the terms included in the third corpus; generating, for each query term in the respective query, a context vector associated with the query term, the context vector having a plurality of context vector elements, each context vector element corresponding to a term included in the third corpus; for each context vector, determining, for each context vector element corresponding to a selected context term in the context vector, a frequency value based on a measure of occurrence of the selected context term in the context terms selected for the query term associated with the context vector; and generating a query vector for the respective query based on the context vectors for each query term in the respective query, the query vector having a plurality of query vector elements, each query vector element corresponding to a term in the third corpus, and each query vector element having a value based on the values of the corresponding vector elements in the context vectors; and determining, from the query vectors for the first query and each respective second query, the distributional similarity score.