1. An electronic Information Retrieval (IR) method, comprising: generating an extended inverted index structure by: generating one or more lexical affinities, wherein each of the one or more lexical affinities comprises two or more search items found in proximity in one or more documents in a pool of documents by: sliding a scan window through each document in the pool of documents to identify search terms and a score for each search term representing an absolute number of occurrences of the search term; sequentially combining one search item being a most top scorer out of a group of content-significant single search items with each other search item in the group, wherein the one search item and the other search item form a pair; and for each pair, comparing document locations for each of the one search item and the other search item forming the pair; in response to determining that a same document ID and an approximately identical offset is found, determining that there is a close relationship between the one search item and the other search item forming the pair; incrementing a counter for each pair that has the close relationship with reference to a location of the one search item and the other search item in a same document from the pool of documents; and creating a top scorer lexical affinity list based on the counter for each pair; and generating a posting list for each of the one or more lexical affinities, wherein each posting list is associated with a specific lexical affinity and contains document identifying information for each of the one or more documents in the pool that contains the specific lexical affinity and a location within the document where the specific lexical affinity occurs.
1. An electronic Information Retrieval (IR) method, comprising: generating an extended inverted index structure by: generating one or more lexical affinities, wherein each of the one or more lexical affinities comprises two or more search items found in proximity in one or more documents in a pool of documents by: sliding a scan window through each document in the pool of documents to identify search terms and a score for each search term representing an absolute number of occurrences of the search term; sequentially combining one search item being a most top scorer out of a group of content-significant single search items with each other search item in the group, wherein the one search item and the other search item form a pair; and for each pair, comparing document locations for each of the one search item and the other search item forming the pair; in response to determining that a same document ID and an approximately identical offset is found, determining that there is a close relationship between the one search item and the other search item forming the pair; incrementing a counter for each pair that has the close relationship with reference to a location of the one search item and the other search item in a same document from the pool of documents; and creating a top scorer lexical affinity list based on the counter for each pair; and generating a posting list for each of the one or more lexical affinities, wherein each posting list is associated with a specific lexical affinity and contains document identifying information for each of the one or more documents in the pool that contains the specific lexical affinity and a location within the document where the specific lexical affinity occurs. 8. The method according to claim 1 , wherein probabilistic counting is used to count a number of occurrences of each of the one or more lexical affinities in the pool of documents at indexing time.