How NLP Makes Semantic Search More Intuitive and Accurate

Marcel Deer - Writer for Unleash
By Marcel Deer
Davor Štefanović - Editor for Unleash
Edited by Davor Štefanović

Published April 3, 2023.

A symbolic representation of NLP-boosted semantic search

Natural Language Processing, or NLP, is a branch of Artificial Intelligence designed to allow computers to process and analyze large amounts of natural language data. Simply, it allows computers to understand language like a human would.

Now, with the help of machine learning technologies, NLP can convert natural language, with all its flaws, into a format that machines can comprehend.

An organization that uses NLP for search queries sees enormous benefits. This is because a natural language processing search engine is designed to understand a searcher’s query and the context surrounding it.

A semantic search, meanwhile, is understood as a search engine’s attempt to generate the most accurate results possible by understanding searcher intent, query context, and the relationship between words.

With tasks that involve normalizing text and typo tolerance, NLP techniques can help make a semantic search more accurate in several ways.


We know that language is complex. Across languages, each word has several variations, from tenses to superlatives to capitalizations. This is why we tend to add a lot of “randomness” when typing in search queries.

Text normalization, one of the key techniques of NLP, is a process that attempts to translate and present these “random” keywords to a machine.

Using NLP technologies, users can type in a query that’s not in the same format as the matching words and still find what they’re looking for. So creating a standard can help connect concepts and simplify language for a machine.

Normalization also increases recall, which in this case is understood as a search engine’s attempts to find results that are known to be good.

One example of how this works is letter normalization: In English, words are capitalized at the beginning of a sentence, whereas in German, all nouns are capitalized. While these rules are helpful for grammar, they make no difference in an information retrieval context, as the meanings of words don’t usually change when they are capitalized. So an NLP would essentially convert all letters to the same case, making it easier for a search engine to process results.

Another common technique for NLP normalization is stemming, which reduces words to their root form. Yet another involves cutting large chunks of text into smaller pieces, called tokens.

When we apply such normalization techniques to language, semantic searches become more effective since variations of words with the same meaning can be grouped together.

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Typo Tolerance

Typos can happen for several reasons.

It could result from human error, a lack of language fluency, or simply hitting the wrong key. It could also result from a poor speech-to-text understanding tool.

In the case of typos, finding the proper context matters the most, which is where NLP techniques can step in.

NLP algorithms can help detect and correct spelling mistakes, making search queries easier to understand by a machine.

But it's not just search queries that suffer from the issue of typos. When a document being searched for is made of user-generated content, it also runs the risk of having several typos. It's essential to fix this since if a search engine looks through a typo-heavy query, it may miss important information or struggle to provide accurate results.

For this reason, NLP typo tolerance must be used across both queries and documents. Overall, typo tolerance is an essential feature of NLP technologies that helps improve the accuracy and usability of a semantic search.

Entity Recognition

Named Entity Recognition (NER) is another useful NLP technology that could help make semantic searches more efficient.

A NER helps a machine identify all named entities and classify them into more refined categories, such as the name of a person, the name of an organization, and the name of the location, among others.

By identifying key terms, or “entities,” in large amounts of text and grouping them, NER helps a semantic search to return more accurate results.

Since NER can automatically tag documents, it can create an index of named entities at the time of ingestion rather than waiting until the search query has been submitted. This index, and moving the task from query time to ingestion time, helps improve the quality of search results.

Besides that, NER is also valuable for determining intent. This involves going beyond understanding a specific query and determining the action a user wants to take when they type in the search. For example, when a user types in “blue skirt,” NER could trigger results related to the user’s actual purchase intent.

Enhancing Semantic Search with NLP

NLP is a powerful tool that significantly enhances semantic search capabilities. By analyzing and understanding the true meaning behind natural language, NLP enables computers to identify relevant content more accurately. Through the application of techniques such as normalization, typo tolerance, and entity recognition, NLP addresses the challenges of language complexity, spelling errors, and contextual understanding, resulting in more accurate and relevant search results.

NLP greatly improves the accuracy and effectiveness of semantic searches, delivering more relevant results for even the most vaguely worded queries. By leveraging the capabilities of NLP, organizations can streamline their search experience and boost productivity. To explore these benefits firsthand, try Unleash—a user-friendly platform that offers GPT-powered answers to search questions. Sign up and try it for free today.

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