Overview of Indexing in Text Preprocessing

Introduction

Indexing helps us create and maintain unique identifiers for individual words, characters, or other linguistic units within a text corpus for efficient retrieval, manipulation, and storage of textual data. When dealing with a lot of data, we might want to retrieve it efficiently for later manipulation. Indexing becomes crucial in such an instance.

Applications of indexing

Here are some common scenarios where we use indexing for text preprocessing:

• Feature extraction for machine learning: When performing feature extraction for machine learning, we use indexing to convert words into their corresponding indexes, which are then used to represent the text in a numerical format that machine-learning algorithms can work with.

• Document retrieval and search: When retrieving data, indexing helps create an inverted index, which maps words to the documents that contain them. This speeds up searching and retrieving relevant documents based on keyword queries.

• Text similarity and clustering: By representing documents as vectors of indexes (or term frequencies), we can measure the similarity between documents using techniques like cosine similarity. This is often used in clustering, topic modeling, and ...