Latest Trends in Language Processing to Make Semantic Search More Semantic

Google, Amazon, and Netflix, for example, have long used semantic search as a significant component of their technology stacks. The current democratisation of these tools has sparked a search renaissance with firms from every industry discovering and using these once-guarded technologies. This spark opens a vital recipe for a wide range of applications as well as products. Semantic search is already widely used by search engines, autocorrect words, sentence translation, any recommendation systems, etc. In the keyword-based search, people seeking enormous collections of documents for uncertain answers is a productivity loss. How can we reduce this problem? The answer can be found in semantic search, notably the question-answering (QA) variant. We can use semantic search to find information based on concepts and ideas rather than keywords. A semantic search tool retrieves the most semantically related terms from a repository when given a phrase. In this chapter, we discuss the various trends in question answering by searching using natural language. Also, we discuss what are the various forms of question and answering, the various components of this question and answering, and what are the places we can use this.