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NLP for Information Retrieval: Techniques for Document Search and Retrieval

Retrieving information is a method in which term-based document retrieval is improved through the application of cutting-edge natural language processing algorithms.

Our system’s core is a conventional statistical engine that creates inverted index files from pre-processed texts and then searches and ranks the documents in response to user queries.

Natural language processing is used to

  • pre-process the documents to extract content-carrying terms from the texts
  • identify cross-term relationships and create a database-specific conceptual hierarchy,
  • transform the user’s natural language inquiries into efficient search queries.

Natural Language Processing (NLP) – An Overview

By using various verbs and nouns, we all learn to match the plural or singular forms and the correct way to express them. Our main ability to build a query, a directive, or a phrase is something that we solely develop.

The underlying premise of NLP is that we can really characterize all these kinds of patterns and describe them to a computer. Once done, then we can teach the computer how we speak and comprehend one another. Research in linguistics and cognitive science plays a key role in this work.

Information Retrieval – A Brief Overview

Information retrieval is basically the process of using context-based indexing to easily access and retrieve the most pertinent information from text, depending on a specific query provided by the user. One of the most popular examples of information retrieval is Google Search.

In order to precisely retrieve the set of documents that respond to a user’s query, an information retrieval system searches a library of natural language texts. They started out as library systems.

These tools help users locate the data they need; nevertheless, they do not make an effort to infer or come up with solutions. The user is informed of the existence and location of papers that may contain the necessary information. Such documents are referred to as relevant documents if they meet the user’s needs. It will only retrieve pertinent documents if our IR system is flawless.

Basics of IR Systems

It is obvious that a user requiring information will need to express their request in the form of a natural language query. Next, after retrieving the pertinent output, in the form of papers, regarding the needed information, the IR system will return the output.

These are the steps involved in using these systems:

  • The process of indexing the document collection.
  • Similar to how the document’s content is represented, the query is transformed.
  • Evaluating the correspondence between the query’s description and each document’s.
  • Listing the results in order of relevancy.

The main two processes in retrieval systems are:

  • Indexing
  • Matching


It is the procedure of choosing words to describe a text.

Indexing involves:

  • Tokenization of string
  • Removing frequent words
  • Stemming

Two common Indexing Techniques:

  • Boolean Model
  • Vector space model


It involves calculating how similar two text representations are to one another.

Wrapping Up

So, this was all you needed to know about the use of natural language processing for information retrieval. Being a subsidiary of Sambodhi Research and Communications Pvt. Ltd., Education Nest is a global knowledge exchange platform that empowers learners with data-driven decision making skills.

Enroll in our insightful courses to dig deep into the vast field of NLP. Connect with us to explore more about our services today!

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