In recent years, NLP (natural language processing) has seen several breakthroughs in helping computers understand human language. A considerable number of open source programs and SaaS provide the NLP analysis to assist users with no coding knowledge to help them communicate with the machines. Among bright examples of such cases are Grammarly, Copy.AI, Hemingway, and other programs working with grammar check, IT/FT analysis, and AI content analysis. Among other examples are search engines, so-called machine translation, sentiment analysis, reference systems, and semantics analyzers with spam recognition integrated into email agents. You can realize your own project in NLP with the assistance of professional software developers and artificial intelligence consultants. Let’s look at the technologies behind the NLP.
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What is Natural Language Process (NLP)?
A subfield of AI, Natural Language Processing, focuses on the interaction between a computer and a human. To do this, software engineers develop a set of commands to help computers recognize the language structures.
With NLP, computers, in particular, can read, interpret and understand human language to gain more valuable results. Processing is generally based on the level of machine intelligence that deciphers human messages into meaningful communication.
Classification
This supervised machine learning model recognizes to what class the data element is related). Let’s take a look at the most common uses of classification in text analysis:
✔ predict positive, neutral, or negative reviews;
✔ recognize adult content or profanity;
✔ stop spammed emails or add penalties to the low-quality articles.
Data is a king
Gathering and cleaning data is not the favorite pastime of the average NLP player. However, it’s a nearly inevitable aspect of any ML preparation. The only possible way to help the researchers with data labeling is the introduction of self-supervised learning, which partially automates the task of data scientists and ML researchers.
So how can you create valuable training data without labeling every text in your database? While the range of options is endless, we will focus here on three proven solutions: active learning, semi-supervised learning, and the weak supervisor. (Link)
Models rule the world
Transfer learning has taken center stage in NLP for the past year or so. In transfer learning, models use the knowledge they learned elsewhere rather than labeled data for a specific task. It can be the labeled data, provided by the scientists and unlabelled text such as Wikipedia articles or a vast collection of texts scanned from the Internet.
Working with data and models pays off. With semi-supervised learning and loose supervision, you can better use unlabeled learning data for specific tasks. With transfer learning, you can even use unlabeled shared training data.
Natural language processing continues to be heavily focused on the English language in academia and industry. Modeling for resource-constrained languages or specialized domains is often too expensive. The solutions I’ve reviewed here can change that: they make data labeling much cheaper and help us build more robust models with fewer data. Let’s hope they expand the horizons of natural language processing.
