Let's say it's evening and you're hungry.You sit down at your computer or pull out your phone to find out where to get food. Maybe you type in, "where should I eat tonight." Or you use a voice search to ask the same question. A decade ago, you would have gotten some pretty strange results based on keywords. But with today's technology and the help of natural language search (NLS), the answers are going to reflect your location and the typical foods you like to eat and likely feature restaurants that are currently open. You see, natural language processing (NLP) powers NLS — and it uses previous searches and related data to determine your intention and give you the most relevant results.
Natural language search relies on AI to help turn a user query made in a native language into a machine-friendly search query. This is done through parsing, which is breaking down sentences into components to find the overall meaning by identifying important words. Through parsing, a natural language search is able to determine which entities are the best match for the query.
On a deeper level, after a natural language search has finished parsing, it begins stemming to extract base forms of words. The natural language search process also includes lemmatization, which is similar to stemming but differs slightly by reducing words into their most basic form. The result of parsing, splitting, and lemmatization is increased retrieval accuracy, which helps users get the right answers to complex questions more quickly. Fueled with natural language processing, you get results far more suitable for the user.
While SEO can be a part of natural language search, they are technically two separate search methods. Keyword queries crawl web pages for keywords, then indexes each page as an entry. The search returns entries that contain the keyword(s) the most amount of times. Ultimately, a search like Yext's Search that uses both keyword and natural language search is ideal.
Natural language search offers brands numerous benefits, like deciphering user intent so visitors can find what they want without knowing the exact details. It empowers customers to search conversationally and rewards them with a faster search that doesn't require that they apply filters. In essence, it's a bridge between user and machine — allowing for accurate and relatable results to questions and searches — like a list of restaurants the user actually would like for dinner.