Index Text For Free

Text indexing is the act of processing a text in order to extract statistics considered important for representing the information available and/or to allow fast search on its content.

In MongoDB, we can create text indexes using db. collectionName. createIndex() method. So, to index a field that contains either string or an array of string elements, pass a document in the createIndex() method that contains the field and the string literal(i.e., ?text?).

In MongoDB, we can create text indexes using db. collectionName. createIndex() method. So, to index a field that contains either string or an array of string elements, pass a document in the createIndex() method that contains the field and the string literal(i.e., ?text?).

You can create a text index on the field or fields whose value is a string or an array of string elements. When creating a text index on multiple fields, you can specify the individual fields or you can use wildcard specifier ( $** ).

Full-text queries perform linguistic searches against text data in full-text indexes by operating on words and phrases based on the rules of a particular language such as English or Japanese. Full-text queries can include simple words and phrases or multiple forms of a word or phrase.

Text index izes and stems the terms in the indexed fields for the index entries. text index stores one index entry for each unique stemmed term in each indexed field for each document in the collection. The index uses simple language-specific suffix stemming.

Definition. Text indexing is the act of processing a text in order to extract statistics considered important for representing the information available and/or to allow fast search on its content.

In MongoDB, we can create text indexes using db. collectionName. createIndex() method. So, to index a field that contains either string or an array of string elements, pass a document in the createIndex() method that contains the field and the string literal(i.e., text).

Improvements can be created by first implementing frequency analysis, stemming,and filtering stop words, and then utilizing hashing methods and superimposed coding techniques to encode the list of methods into bit representation.

The indexing stage will scan the text of all the documents and build a list of search terms (often called an index, but more correctly named a concordance).The indexer will make an entry in the index for each term or word found in a document, and possibly note its relative position within the document.