The Problem With Semantic Search

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Semantic search is an approach to retrieving information on the web that involves understanding the contextual meaning of search terms. Traditional search engines, like Google in its early days, relied on keyword-based search, where the results were based on the exact match of the search terms. However, semantic search goes a step further by understanding the intent behind the search query and the contextual meaning of the terms, providing more relevant and accurate results.

For example, if you search for “Apple”, a keyword-based search engine might return results about the fruit, the tech company, and possibly even the record company. However, a semantic search engine would analyze the context in which you’re searching to provide more relevant results. If your previous searches were about smartphones, it would prioritize results about Apple Inc., the tech company.

Semantic search uses various techniques including natural language processing (NLP), machine learning, and semantic understanding of text. It can involve understanding synonyms, homonyms, context, natural language queries, and even user behavior analysis.

Let’s look at a simple example of how semantic search can be implemented using Python. We’ll use a Python library called NLTK (Natural Language Toolkit) for this purpose.

import nltk
from nltk.corpus…