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hello and welcome to this session on information retrieval today our focus is on index compression before we get into index compression it is useful to know some statistical properties of terms specifically we will discuss the rule of 30 heaps law and zipamp;#39;s law and then we move on to index compression and as we already have seen our inverted index is made up of dictionary and postings we discuss index compression in two parts on how to compress the dictionaries and how to compress the post x letamp;#39;s start with the rule of 30. it says that the 30 most common words account for 30 of the s in return text and this gives us uh clues of what we call as the stop words list right the most common words uh make up for the bulk of the content so given a collection of documents how do we estimate the number of terms in the document often this kind of an estimate is useful uh to plan for letamp;#39;s say buying the hardware infrastructure or even to wonder how much of compression do