How do you use semantic searching?

Here’s a quick list of what you need to do as a result of semantic search:

1. Worry less about exact keywords.
2. Make sure that every piece of content you produce has a clear focus.
3. Create high quality content.
4. Understand user intent.
5. Don’t stuff your content with keywords.
6. Use structured data markup.

What are the types of searches in Naukri?

Click Search. Use filters such as Top Companies, Industry, Salary range, Location, Education, Employer type (posted by company or hiring consultancy), Job Type (International, Premium, Walk-in) and Freshness (how long ago the job was posted) to get more accurate results.

What is semantic search and how does it work?

Semantic Search is primarily based on two concepts: Search intent of the user: This implies understanding the reason why the user has asked the particular query. This could be anything from wanting to learn, find or buy something, etc. If the intent is understood well, search engines can provide the most relevant results to the users.

What are the limitations of traditional keyword search?

Traditional keyword search does not include the lexical variants or conceptual matches to the user’s search phrase. If the exact combination of the words used in the user’s query is not present in the overall content, irrelevant results would be returned to the user.

What is the relationship between the words in the search phrase?

Relationship between the words in the search phrase: It is important to understand the meaning of all the words together in the search phrase rather than the individual words in them. This means understanding the relationship between those words thus displaying results that more conceptually similar to the user’s query.

How to do semantic similarity search in Elasticsearch using Python?

These vectors can be indexed in Elasticsearch to perform semantic similarity searches. Many techniques are available today in python to convert text to vectors like — bag-of-words, Latent-Dirichlet-Allocation (LDA), n-gram embeddings, Doc2Vec, etc.

https://www.youtube.com/watch?v=jxNpxT7Eq8Q