The adoption of several worthy to mention technologies played a fundamental role in this evolution. Natural language processing (NLP) has already started to make a big impact in search, and its take-up is likely to accelerate in the coming years. NLP takes search away from the simplistic "is this keyword present in the title or description" approach, and starts asking "what is the customer really asking for?". By taking a semantic angle to evaluating search results, the results produced are far more accurate and relevant to the customer's actual search intent. Factor in the self-learning capabilities of many NLP-based engines, and suddenly site search starts to look very different.
Natural language processing (NLP) refers to the capacity of a computer or machine to accept, analyze and generate human speech. The ultimate aim is to make the interaction between humans and computers and human to human possible. Although in our modern world we are equipped with artificial intelligence ( A.I) and machine learning devices, but lack of proper communication has always been a problem among people. We do not understand each other although we speak the same language. I have been working on a project so that we will be able to understand what we really mean when we interact with each other. If you pay a close attention, you will know that this lack of communication has created war between governments, organizations, families, classmates, friends and couples. In my opinion we need to improve the natural communication among humans and create a reform in language structure. In this article one is going to have an overview on NLP, its classification, weaknesses and finalize the dicussion on voice search in search engine.
Forrester, one of the leading analyst firms, defines Cognitive Search in a recent report¹ as: The new generation of enterprise search that employs AI technologies such as natural language processing and machine learning to ingest, understand, organize, and query digital content from multiple data sources. Here is a shorter version, easy to memorize: Cognitive Search Search NLP AI/ML Of course, "search" in this equation is not the old keyword search but high-performance search integrating different kinds of analytics. Natural Language Processing (NLP) is not just statistical treatment of languages but comprises deep linguistic and semantic analysis. And AI is not just "sprinkled" on an old search framework but part of an integrated, scalable, end-to-end architecture.
If you've been using voice search technology from Apple Siri, Amazon Alexa, or Google Home more than you did in previous years then you're a part of a large and growing trend that's impacting the way we search for information and make purchases. In fact, Dominos knows this very well, and allows customers to order a pizza just by speaking into a smart device.
Interacting with search systems, such as Web search engines, is the primary means of information access for most people. Search providers have invested billions of dollars developing search technologies, which power search engines and feature in many of today's virtual assistants (including Google Assistant, Amazon Alexa, Microsoft Cortana, and others). For decades, search has offered a plentiful selection of research challenges for computer scientists and the advertising models that fund industry investments are highly lucrative. Given the phenomenal success, search is often considered a "solved problem." There is some truth to this for fact-finding and navigational searches, but the interaction model and the underlying algorithms are still brittle in the face of complex tasks and other challenges, for example, presenting results in non-visual settings such as smart speakers.15