35. The computer system of claim 34 , wherein whether the document has been identified as a spam document is further based on: determining, for a second phrase contained in the document, a number of the related phrases related to a second phrase expected to be present in the document; determining for the document, and for the second phrase in the document, an actual number of related phrases present in the document; determining, for a third phrase contained in the document, a number of the related phrases related to a third phrase expected to be present in the document; determining for the document, and for the third phrase in the document, an actual number of related phrases present in the document; and identifying the document as a spam document when the actual number of related phrases present in the document for any of the first phrase, the second phrase, or the third phrase exceeds the expected number of related phrases for the respective first, second, or third phrase.
34. A computer system comprising: one or more memories configured for storing executable instructions; and one or more processors configured for executing the instructions, wherein the instructions include instructions to: receive a search query; retrieve a set of documents relevant to the search query, each document having a relevance score; determine, for each document in the set of documents, whether the document has been identified as a spam document; down-weight the relevance score of the document in response to a document being identified as a spam document; and organize the set of documents by their relevance scores, wherein the relevance scores by which the documents are organized include down-weighted relevance scores for documents that have been identified as spam documents, wherein whether the document has been identified as a spam document is based on: determining, for a document that contains a first phrase, a number of related phrases related to the first phrase expected to be present in the document; determining for the document, and for the first phrase in the document, an actual number of related phrases present in the document; and identifying the document as a spam document by comparing the actual number of related phrases present in the document with the expected number of related phrases, wherein determining the number of related phrases expected to be present in the document includes: traversing an index of a plurality of documents; for each of the indexed documents, determining a set of phrases in the document, and for each phrase in the set, determining a number of related phrases also in the document; and determining the expected number of related phrases based on the determined number of related phrases across the traversed documents.