Inverted and Positional Indexing
Learn how to apply inverted and positional indexing using Python.
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Inverted indexing
Inverted indexing is a widely used technique in text processing that involves creating an index data structure that maps terms or words to the documents or records in which they occur. This type of indexing inverts the relationship between terms and documents with the goal of fast and efficient retrieval of documents containing specific terms or words. While this indexing is similar to term-based indexing, it differs in the following ways described in the table below:
Inverted vs. Term-Based Indexing
Aspect | Inverted Indexing | Term-Based Indexing |
Purpose | We use it for efficient full-text search in large collections of documents. | We use it for information retrieval tasks like search engines. |
Indexing Implementation | This type of indexing iterates through each document’s tokens, recording the document IDs where each term is found. | This type of indexing iterates through each document’s tokens, recording not only the document IDs but also the positions where each term occurs within each document. |
Data Structure | Inverted lists or postings lists store document IDs associated with each unique term. | Stores terms as keys and their metadata or positional information within documents as values. |
Storage Efficiency | It’s efficient in terms of storage space, especially for sparse data. | It requires more space as it lists documents for each term. |
Search Efficiency | It’s fast for retrieving documents containing specific terms. | It’s less efficient for text retrieval and often requires additional processing. |
Index Construction Time | It has a faster index creation time due to it’s simpler structure. | It has a longer index creation time because it involves storing metadata. |
Use Cases | It’s used in search engines and in information retrieval systems. | It’s less common due to inefficiency. We normally use it in small-scale applications. |
To get started with this indexing, we first tokenize the text, remove stopwords, sort the resulting terms alphabetically, and then index them with their corresponding documents or records. Let’s apply inverted indexing using ...