This lecture covers the construction of an inverted file for information retrieval indexing, addressing granularity levels, and the use of tries in index construction. It explains the process of searching the inverted file, vocabulary search, and manipulation of occurrences. The lecture also discusses index compression, index merging, and the map-reduce programming model for constructing the inverted file. Additionally, it explores the applications of map-reduce frameworks in various tasks, such as graph processing and learning probabilistic models.