NLP algorithms are an essential part of search because they bridge the gap between human communication and machine understanding. This enables search AI to understand what is being asked of it and to deliver results that are relevant and contextual to the query.
Using NLP, the search results will be more aligned with the user’s intent, and the algorithm will be able to handle complex queries by understanding more nuanced requests. This is because it can identify sentiment and understand the context, as well as personalize the search experience based on previous conversations with the user.
Word embeddings
One of the ways an algorithm can work with words to find similarity is with word embeddings, where words and assets are represented as vectors. This is where it analyzes unstructured data like text and images and transforms them into a numeric value.
A popular example of this is Word2vec, an algorithm that learns word embeddings from a huge collection of written texts. It then analyzes the surrounding text to determine meaning and understand context. Another example is GloVe (Global Vectors for Word Representation), which is also trained to build connections between different words by mapping them depending on their semantic similarity.
Language models
There are also language models that analyze large amounts of data in order to accurately predict the likelihood of what order words will appear. Or in simpler terms, they’re algorithms that allow the search AI to not just understand what we’re saying, but also be able to respond in a way that matches how humans communicate.
For example, BERT (Bidirectional Encoder Representations from Transformers) is a popular language model that’s able to understand complex and nuanced language, which can then be used for powerful semantic search and question answering.
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