10 Questions
What is the predicted number of terms according to Heaps' law for the first 1,000,020 tokens?
38,323
What is the actual number of terms for the first 1,000,020 tokens?
38,365
Why is compressing the dictionary important?
To keep it in memory and for competition with other applications.
What is the limitation of using fixed-width entries for the dictionary?
Most of the bytes in the term column are wasted due to the fixed width allocation.
How is the dictionary stored as a string, and what is the space requirement for this method?
The dictionary terms are stored as one long string of characters, with term pointers marking the end of the preceding term and the beginning of the next. The space requirement is 7.6MB.
Why is compressing the dictionary important in information retrieval?
To make it small enough to keep in main memory and to reduce disk space needed for the postings file.
What is the difference between lossy and lossless compression in the context of information retrieval?
Lossy compression discards some information, while lossless compression preserves all information.
What is Heaps' law, and what does it indicate?
Heaps' law is represented by M = kT^b, where M is the size of the vocabulary and T is the number of tokens in the collection. It indicates that the size of the vocabulary grows with the collection size.
What are the typical values for the parameters k and b in Heaps' law?
Typical values for the parameters k and b are: 30 ≤ k ≤ 100 and b ≈ 0.5.
Why can't we assume there is an upper bound for the distinct words in the term vocabulary?
The vocabulary will keep growing with the collection size, and there is no fixed upper bound due to the nature of the language and the increasing collection size.
This quiz covers the concept of index compression in the context of information retrieval. It discusses the importance of compressing the dictionary and its impact on memory and speed.
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