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Natural language search (NLS) is a technology that enables computers to understand natural human speech. NLS uses algorithms to analyze words and phrases spoken aloud by humans. The computer then interprets these words and phrases based on its understanding of the context in which they were said.

This allows users to type queries in any order and receive results that match those queries. This makes searching far faster than traditional methods, which require users to enter text-based queries in a specific sequence.

There are two types of NLS: Natural Language Processing (NLP) and Natural Language Generation (NLG).

NLG refers to technologies that allow computers to generate text based on user input. Examples include chatbots, voice recognition software, and virtual assistants.

NLS is often used in conjunction with artificial intelligence (AI), machine learning (ML), and deep learning (DL). AI is a field of study concerned with making machines exhibit intelligent behavior. ML involves training computers to learn from examples. DL involves creating neural networks that mimic the brain’s ability to process information.

These three fields work together to create powerful tools that can perform tasks previously thought impossible.

For example, Google Translate is powered by NLS combined with AI.

Here are some of the most common applications of NLS:

  • Voice Recognition Software - Allows computers to interpret audio recordings.

  • Chatbots - Automated programs designed to converse with humans via messaging platforms.

  • Virtual Assistants - Programs that automate routine tasks and answer questions posed by humans.

  • Conversational User Interfaces - Tools that enable computers to interact naturally with humans through speech, writing, gestures, etc.

  • Machine Translation - Technologies that translate written content between languages.

  • Speech Synthesis - Technology that generates audible speech from text.

Natural language understanding (NLU) is another critical aspect of NLS.

NLU is used to interpret the meaning of the query and determine whether the answer provided is correct. This technology helps the system understand the context of the question and identify the answer.

What are the benefits of NLS?

Benefits of NLS include:

  1. Easy to use - Users can easily type their questions in any format. They don’t need to learn complex syntaxes or fill out forms.

  2. Relevant Results - The system understands the context of the question asked and returns only those results that match the query.

  3. Better User Experience - The system doesn’t require users to enter long strings of characters or fill out complicated forms. Instead, users simply type in their questions and receive immediate responses.

  4. No More Keywords - Since the system understands the questions being asked, there is no need for users to specify keywords when searching.

  5. Improved SEO - Since the system understands user intent, it can be optimized for better ranking in SERPs.

  6. Less Data Entry - There is no need for users to enter data manually since the system automatically extracts the required information from web pages.

  7. Reduced Costs - Since the system does not require manual entry of data, it saves costs associated with hiring human resources.

  8. Increased Conversions - Since the system understands users’ intent, it can provide them with accurate answers. Therefore, users are more likely to convert.

Natural Language Search on Google

Conversation is one of the most critical aspects of human communication. We use language to communicate our thoughts, feelings, opinions, ideas, etc., and we are constantly learning about each other through conversation.

In an online search, however, there is no such thing as “conversation”. If you want to find information about “how to make money”, you’ll probably enter the keywords into the search bar. You won’t see anything else unless you add additional words to narrow your search.

But what if you could actually talk to Google? What if you could ask questions and receive answers directly from the search engine itself? And what if those answers came in the form of natural language text?

This is exactly what happens when you perform a conversational search.

The idea behind conversational search is simple. Instead of asking Google to provide you with a list of possible matches, you’re now telling Google what you’re looking for.

For example, let’s say you’re searching for “best coffee shops in San Francisco.” When you start typing, you might notice that Google provides suggestions based on your previous searches. So far, so good.

However, once you’ve entered some keywords, Google will begin suggesting questions for you to answer. These questions are designed to help Google understand your intentions better. For example, if you tell Google that you’re looking for a place to buy coffee beans, it will suggest the following question: “What do you mean by ‘buy’?”

The problem is that there aren’t many tools out there that let you easily conduct natural language searches. You’d think it would be easy since we already use natural language processing (NLP) to understand speech. But NLP isn’t good enough yet to answer complex questions.

So how does Google handle these kinds of questions?

They use machine learning to analyze billions of web pages and documents to train algorithms to recognize patterns within text. Then, when someone asks a question, those algorithms look for similar questions and provide relevant answers.

This is why you see some of the same questions pop up over and over again on Google. Those questions are answered by trained algorithms that know the context of each question. And that’s where things get interesting. Because now, you don’t just get one answer; you get multiple answers. Each answer is ranked according to how well it matches your question.

If you asked, “Why did President Trump fire James Comey?” Google might return several possible reasons. One reason might be “Because he wanted to save face.” Another reason might be: “He thought Comey wasn’t doing his job.” Or maybe it’s “Trump didn’t trust him anymore.” The algorithm ranks each answer according to how likely it is to be correct.

So, what happens next? Well, you decide which answer is most accurate.

After all, you wouldn’t want to believe something that’s wrong. But you also don’t want to ignore the facts. So, you read both sides of the story. You weigh the pros and cons of each option. And finally, you choose the answer that makes the most sense

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