Search engines have grown in popularity, usage, and abilities over the last couple of decades. Slowly but surely, they have become an integral part of our lives. As a result, every search engine user expects fast and personalised results.
Conventional search engines that relied on string-based keyword matching helped find information, but they suffered on one crucial front - understanding the semantics and context of the search queries. Due to this, the results received were often not as optimised as they should be. Luckily, studies in human cognitive processes have helped us unearth important fundamentals about ourselves, which has aided in developing advanced technologies and strategies to (try and) make machines as intelligent as us.
Natural Language Processing is one such subfield that derives from the intersection of linguistics, psychology, and computer science and aims to process, understand, and generate human-like Natural Language. Note that ‘Natural Language’ refers to the everyday language we employ to communicate.
Natural language search allows users to use text or voice to input their search query using everyday Natural Language phrases. This essentially means that users no longer need to search for keywords - they need to communicate to the search engine in a way they would communicate with another human. The algorithm will understand the context and semantics and transform the query into meaningful, relevant results.
In this article, let’s understand how NLP has revolutionised site search engine operations by providing semantic search and how it is the way forward!
How does Natural Language Processing (NLP) based search work?
Natural Language Search uses a technique known as Natural Language Processing to process vast amounts of data, run statistical and machine learning models, and infer meaning from complex sentences. Naturally, this is a much more feasible approach to building search engines, especially since there is no shortage of data today. Also, computing powers have improved substantially to help NLP do its wonders.
The real strength of NLP-based search comes from its ability to syntactically parse different sentence structures and break down compound and contextual statements into meaningful chunks. For instance, if you queried an NLP-based search with something like - “What size shorts can I buy for my 10-year old son?” - the algorithm will be able to determine that you are looking for shorts for male children around the age of 10 and you want to know what all shorts are in stock. Based on this understanding, the search engine will give you relevant results.
Before penetrating the search engine domain, NLP had been widely popular and valuable for communicating with a personal assistant and gathering basic facts. However, as people got increasingly comfortable with NLP-based voice assistants, it became clear that it would be a good idea to give consumers the same powers of NLP in their search engine experience. Eventually, it became essential for growth-oriented businesses to change their traditional keyword-based search with NLP-based semantic search.
Let’s look at the core components of NLP-based semantic search that make it stand out, especially when compared to keyword-based string search methods.
Core components of NLP-based search
Human language is not 100% precise, making it difficult for machines to comprehend and extract information from our natural form of communication. However, with NLP, it has become possible to imbibe our ability to understand the intent and context of communications into machines.
NLP helps extract meaning from human language by detecting intent and context. Then, it gives relevant responses based on the performed analysis. All in all, NLP makes human-to-machine interactions possible and seamless using the following core components:
1. Natural Language Understanding (NLU) - to understand the user’s intent
Intent, especially in user searches, means what a user is trying to find by performing a search query. The intent could be anything - from exploring information to learning, making purchase decisions, comparing products, etc. Picking out users’ intent from their search queries is the role of NLU. For that, NLU leverages:
- Topic analysis - this includes deciphering the intent of what a user asked. The ability to understand the intent of the topic opens a lot of room for expanding the search results and arriving at more precise answers.
- Context extraction - helps the algorithm understand the context of the user query. Context is often very important when it comes to searching for queries. The term “sick” could mean different things in different contexts. In the world of online gaming, it could mean something positive, but in healthcare, it almost always means something bad. So, the ability to understand context makes it possible for the algorithms to give better, more accurate results.
- Syntactic analysis - this is to analyse the structure of the sentence to break it down into smaller chunks and extract the dictionary meanings. This makes the whole process of querying and returning results faster.
- Identity identification - is used to identify important entities - like organisation name, person name, place name, and so on, from the search query and find out how important they are to the entire query. By leveraging this information, the search results can be refined, made more personalised, and improved in other ways too. It can be ensured that no fluff reaches the user.
- Semantic analysis - is used to decipher the contextual meaning in cases where it has multiple possible implications. This is important to come to the closest possible meaning of the search phrase in situations where there are multiple possible meanings.
- Sentiment analysis - this dives deeper into the search query by identifying the user’s mood, feelings, etc. This information can be leveraged to provide users with relevant results.
2. Machine Learning - to find and learn nuances in customer behaviour
Unlike traditional algorithmic programming, machine learning models are created to be able to successfully deal with new cases. The goal of machine learning is to train models by feeding them with quality data and helping them explore and find patterns. Clearly, processing and understanding natural language is all about finding patterns in sounds and recollecting what the semantics of those sounds is. That is how we humans process and go about understanding sentences. So, it seems intuitive that machine learning is a good approach to help with NLP, too.
Machine learning for Natural Language Processing is all about using different statistical tools in order to identify parts of speech, sentiments, entities, and other important aspects of the text by finding patterns. This can then be expressed as a model that can be applied to new sets of input. Machine learning, in whichever form, has helped Natural Language Processing and performs a crucial role in semantic search, too.
3. Natural Language Generation (NLG) - to generate the response
This stage is about getting the machines to produce an output that is easily understandable by humans. The Natural Language Generator (NLG) system acts as a router to reflect human-like conversations and communicate our thoughts in a consumable language. This makes the exchange of thoughts more fluid, relevant, and natural. For this, the following steps are used:
- Sentence creation - the machine-generated code is used to generate natural language sentences.
- Document creation - individual sentences are structured based on meaning and context to form one comprehensive narrative.
- Text summarisation - for getting a summary or synopsis of large bodies of texts without losing any meanings.
In the case of search, this core feature is used to enhance the attributes of specific product listings or even a single product and can come in handy when site search is placed on a marketplace, sourced from a lot of content generated by people or businesses. In short, the system continually improves on regular mundane processes and transforms them into elaborate responses as opposed to doing it manually.
NLP-based search takes all the required steps to facilitate human-machine communication in a way that resembles human-human communication. This single reason is enough to make it a superior search approach to keyword-based string-matching search.
But that’s not all - NLP-based search trumps keyword search on several fronts.
Natural Language Processing based search vs Keyword-based string-matching search
Google and other legacy search engines made it possible for people to get accustomed to keyword searches. However, as the information available on the internet grew exponentially, it became clear that keyword search is not an intuitive approach to go about this problem. It has also been found that keyword search is pretty irrelevant in most cases and often does not give users the exact results that they are looking for. Keyword search primes users to use as few words and many keywords as possible without using question words or connective languages. This approach, in turn, also puts more pressure on businesses when it comes to mining intent from keyword searches.
For example, the users of keyword-based search would look up “veg recipe tomato cheese” instead of “What vegetarian dish can I prepare using tomatoes and cheese?”. This leaves no chance for understanding context or intent, and it all boils down to exact keyword matching. However, with the rise of voice assistants, people started getting more conversational with their devices, which prevented them from talking in just keywords. Now, their intent, context, and everything else mattered, too. Further, with NLP, it became possible to search using synonyms, vernacular and mixed languages, which simplified the search process for many users worldwide. In such an evolving landscape, NLP-based search provides the perfect way out for businesses and consumers looking to improve their site search experience.
Are you ready for an NLP-based search experience?
With the available data growing exponentially - both in size and variety - sophisticated algorithms are being developed to leverage this data as best as possible. Natural Language Processing-based search is one such approach that utilises all the user data - from their interaction with virtual assistants to their search history, habits, and so on, to extract the precise meanings of their site search queries and return highly relevant results. Needless to say, this saves a lot of time for users and makes their search experience seamless.
NLP-based search is the present and the future of search engines - and as the heap of data keeps increasing, we will witness more advancements and revolutions in the field of NLP that will simplify the search process even further.