The term vocabulary and postings lists

Abstract

Recall the major steps in inverted index construction: Collect the documents to be indexed. Tokenize the text. Do linguistic preprocessing of tokens. Index the documents that each term occurs in. In this chapter, we first briefly mention how the basic unit of a document can be defined and how the character sequence that it comprises is determined (Section 2.1). We then examine in detail some of the substantive linguistic issues of tokenization and linguistic preprocessing, which determine the vocabulary of terms that a system uses (Section 2.2). Tokenization is the process of chopping character streams into tokens; linguistic preprocessing then deals with building equivalence classes of tokens, which are the set of terms that are indexed. Indexing itself is covered in Chapters 1 and 4. Then we return to the implementation of postings lists. In Section 2.3, we examine an extended postings list data structure that supports faster querying, and Section 2.4 covers building postings data structures suitable for handling phrase and proximity queries, of the sort that commonly appear in both extended Boolean models and on the web. Document delineation and character sequence decoding Obtaining the character sequence in a document Digital documents that are the input to an indexing process are typically bytes in a file or on a web server. The first step of processing is to convert this byte sequence into a linear sequence of characters.