What is contextual semantic search (CSS)?

Ever wondered how the nearest restaurants show up when we search “restaurants” on search engines such as Google? Or how the suggested products shown on e-commerce websites such as Amazon are so personalized. These are all possible through a search technique called contextual semantic search (CSS). Before we learn about CSS (not to be confused with cascade styling sheet used in web development), we have to understand how computers are actually capable enough to understand our searches or queries. The answer to that is using natural language processing or NLP.

It is a field that merges artificial intelligence and computational linguistics to facilitate interactions between humans and computers. Its main aim is to address the complexities of understanding and processing human language through effective techniques. These techniques involve analyzing and generating natural language text or speech, enabling applications like machine translation, sentiment analysis, chatbots, and information retrieval. NLP revolutionizes our interaction with technology with its enhanced language understanding capabilities.

Transitioning to our main focus in this Answer, contextual semantic search, we have to explore further how it works and the applications of this search in this era.

What is contextual semantic search?

Before diving into the details of contextual semantic search, let's look at the more general term, semantic search. Semantic search focuses on understanding the meaning of the search query and its content. Moreover, advances in NLP have made it possible to train a language model on massive datasets, allowing it to search for documents with similar semantic content. This was not the case before the efficient language models, as the search engines just used keyword search.

The way CSS works is that it takes thousands of messages and a concept as input and filters all the messages that closely match the given concept.

Focusing on CSS

Usually, these searches are used after several steps where a query or prompt is analyzed and somewhat classified. Before using CSS, the prompt is analyzed with respect to its sentiment, emotion, and intent. This basically means that any prompt is first classified as positive, neutral, or negative through sentiment analysis. This is further analyzed by identifying the correlated emotion attached to the prompt or document, such as sad, happy, funny, etc. The other way the prompt or document is filtered is through its intent. In simpler terms, this would identify whether any prompt relates to an opinion, news, marketing, complaint, suggestion, appreciation, or query.

Now, after all this filtering, the data becomes easier to work with, making the contextual semantic search work reliably and efficiently. CSS is an intelligent search algorithm designed to improve the accuracy and effectiveness of searching for specific concepts or topics. Instead of relying solely on keyword matching, it considers the context and semantics of the search query to find messages or content. It also simplifies the search process by taking the concept's name as input, and it filters contextually similar messages. Even if the variations or related keywords are not explicitly mentioned.

By incorporating contextual understanding and semantic analysis, CSS offers a major improvement over conventional methods. It allows for more comprehensive and accurate information retrieval, overcoming the limitations of relying solely on predefined keywords. CSS enables a more innovative and effective search experience by considering the context and meaning of the given concept.

Applications

Contextual semantic search has various applications across different domains. Some notable applications of contextual semantic search include:

• E-commerce: Contextual semantic search enhances the search experience on e-commerce platforms by considering user preferences, browsing history, and contextual cues helping users find more relevant products and services

• Content recommendations: Contextual semantic search is used in recommendation systems to suggest personalized content to users based on their preferences, behavior, and current context. It assists in delivering tailored recommendations for articles, videos, music, news, and more, such as the suggested videos on the home page of Youtube. 

• Customer support and chatbots: Contextual semantic search enables chatbots and virtual assistants to understand user queries in the context of ongoing conversations. It helps provide accurate and contextually relevant responses, enhancing the customer support experience.

• Information retrieval: In large-scale document repositories or knowledge bases, contextual semantic search improves the retrieval of information by considering the context and meaning of the search query. It helps users find more precise and contextually relevant information.

• Question-answering systems: Contextual semantic search is utilized in question-answering systems to comprehend user queries and find relevant answers. It considers the semantic meaning of the query and the context in which the question is asked to deliver accurate and context-aware responses.

The best example of how to understand contextual semantic search would be if you ever forgot the name of any song, becoming a mental itch while you try to remember it. And if you don't even remember the song's lyrics, we can sometimes search for the melody, which can help you find it. This is even further assisted, as we can see by the use of Google’s powerful assistant, where we can sing, hum, or even whistle the melody of that song to pinpoint the name of the song.

Let's take a short quiz for a deeper understanding.

Summary 

Contextual semantic search is an advanced approach in natural language processing that interprets the meaning of user queries within their specific context. It goes beyond traditional keyword matching, considering the different interactions to deliver personalized and precise search results. This enhances the efficiency and relevance of interactions with search engines and virtual assistants, making the user experience more natural and tailored to individual needs.