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What is semantic search using NLP?
How do machines answer queries within context and understanding? Answer: semantic search.
Before was SEO keyword search, now is semantic search using NLP techniques.
Natural language is a language that is natural for humans. In machine learning, NLP, NLU, and NLG help machines to understand the subtleties of human communication. Within the 6,500 spoken languages in the world, they are used for both, speech and text for NLP and NLU applications.
Semantic field is the study of meaning in words groups of words. For search engines, keyword search vs. semantic search has changed SEO. As a result, search engines not only can match the exact keywords but also match the meaning and intent to answer a query.
According to the Search Engine Journal “Semantic search describes a search engine’s attempt to generate the most accurate Search Engine Results Page (SERP) results possible by understanding based on searcher intent, query context, and the relationship between words.“
In machine learning, semantic search captures the meaning from inputs of words such as sentences, paragraphs, and more. It implements NLP techniques to understand and process large amounts of text and speech data. This is the pre-processing data stage called text processing. (As we shared in an infographic) It is the process of analyzing textual data into a computer-readable format for machine learning algorithms.
David Amerland describes semantic search as “At its most basic level semantic search applies meaning to the connections between the different data nodes of the Web in ways that allow a clearer understanding of them than we have ever had to date.” He is the author of Google Semantic Search: Search Engine Optimization (SEO).
How to Implement Semantic Search Using NLP and What is a Language Model?
A language model is a tool to incorporate concise and abundant information reusable in an out-of-sample context by calculating a probability distribution over words or sequences of words.
The famed BERT is an example of a state-of-the-art language model. The bidirectional encoder representations from transformers can answer more accurate and relevant results for semantic search using NLP.
It is a well-known state-of-the-art language model created in 2018 by Jacob Devlin and leveraged in 2019 by Google to understand user searches.
The open-source machine learning framework for NLP tasks was pre-trained with Wikipedia’s text. It helped BERT to fine-tuned the question and answer datasets. BERT covers NLP tasks such as question answering and natural language interference (MNLI).
How to get data for language models to train them?
Language models such as BERT need a large amount of corpus data in the targeted language to fine-tune its general understanding of the language. The crucial part here is the data collection and data preparation. Crowdsourcing is an approach to get abundant data from a global crowd in many languages.
It entails facilitating recruiting data collectors at a large scale for multilingual datasets. Quality assurance is managed by the crowdsourcing companies matching the language model need. By using crowdsourced data for building language models you can save time, ensure the diversity and scalability of your datasets.