NLP in machine learning | Techniques & Applications

Natural language processing is part of our everyday lives and is essential to our home and work lives. It allows us to send voice commands to virtual home assistants, smartphones, and even vehicles without having to think. NLP in machine learning as a service is used by speech-activated programs like Alexa, Siri, and Google Assistant to respond to our questions, add events to our calendars, and contact people we've entered in our voice commands. In addition to making our lives easier, NLP in machine learning is revolutionizing the way we work and live. Because of the importance of this topic, we decided to discuss the techniques and applications of natural language processing in this article.

What is Natural Language Processing (NLP)?

Natural Language Processing (NLP) is an innovative field of computer science and artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. This technology has become increasingly important in modern technology, enabling seamless human-machine interaction through natural language.

NLP Techniques

NLP is a very broad field that requires the use of various techniques to successfully process and understand human language. In the next section, we will review and define a number of common techniques in NLP technology.

Tokenization

One of the most crucial and straightforward NLP strategies is this one. In reality, it is a crucial step in the preprocessing process that divides a large string of text into tokens. Tokens include words, characters, and subworlds. They serve as the foundation for natural language processing. Most NLP models process raw text at the token level.

Stemming & Lemmatization

After tokenization, the next preprocessing step is stemming. These techniques generate word roots from the different variations of a word. For example, the root of the word stick can be written in several ways:

Stick, Stuck, Stickers, Sticking, Sticks, Unstick

Root finding and stemming are two different methods of finding the root of a word. Stemming deals with removing the endings of a word. Depending on the word, this NLP method may or may not be effective.

In the more intricate process of lemmatization, the base form of a word—also known as a lemma—is discovered through morphological analysis.

Finding a word's base form, also known as a lemma, is a more difficult process called lemmatization that makes use of morphological analysis.

Morphological segmentation

Morphological segmentation is the process of breaking down words into the morphemes that make them up. A morpheme is the smallest unit of language that carries meaning, and some words, such as table and lamp, have only one morpheme.

But there are other words that can contain morphemes. For example, the word sunrise contains two morphemes: sun and rise. Morphological segmentation, such as rooting and word formation, can help preprocess the input text.

Stop words removal

This technique becomes another NLP preprocessing step that removes filler words to allow the AI to focus on words that have meaning. These include conjunctions such as "and" "because" and prepositions such as "under" "in".

By removing unhelpful words, NLP systems have less data to process and become more efficient. This is not a necessary step for every NLP use case, but it can help with things like text classification.

How Natural Language Processing Can Be Applied

There are many different programs available for NLP. In this section, we will discuss the different ways that companies can use this technology in their business:

Improve the user experience

NLP can be integrated into a website to provide a more user-friendly experience. Features such as spell checking, auto-completion, and auto-correction in search bars can make it easier for users to find the information they are looking for and keep them from leaving your site.

Automated support

Chatbots are not new, but advances in NLP have increased their usefulness and live agents no longer need to be the first point of contact for some customers. Some chatbot functions include helping users navigate support articles and knowledgebases, ordering products and services, and managing accounts.

Monitor and analyze feedback

Between social media, reviews, contact forms, support tickets, and other avenues of communication, customers often leave feedback about a product or service. NLP can help collect and understand all this feedback and turn it into actionable insights that can help improve the business.

The role of NLP in machine learning

The link between machine language, natural language processing, and NLP in machine learning can occasionally be unclear. ML can be used in NLP technology. But there are different types of NLP that work without relying on AI or ML. For example, there is an NLP tool designed for simple extraction of primary data. It can be based on systems that do not require continuous learning by artificial intelligence.

In general, for more complex machine learning NLP applications, systems can use ML models to improve their understanding of natural language. ML models can make it easier to adapt to changes in human language over time. In addition, NLP can employ other systems, supervised machine learning, unsupervised machine learning, both, or neither, to enhance its algorithms.

When used in natural language processing, machine learning can recognize human speech patterns, understand emotional context, receive textual cues, and learn any other component of text or audio input. More complex applications that require a high level of understanding to have an intelligible conversation with humans require ML to make this possible.

Applications of Deep Learning for Natural Language Processing

Text marking and classification

Tokenization is the process of breaking words into symbols and pieces that machines can understand. English documents are easy to tokenize because they contain spaces between words and paragraphs. It should be noted that most other languages present new challenges; for example, logographic languages such as Mandarin and Japanese Kanji can present different challenges because they have no spaces between words or even sentences.

Creating Captions for Images

It's difficult to automatically describe an image's content using natural language. In addition to recognizing the objects in the image, the title of the image must also express the way they relate to each other and their characteristics. In addition, semantic knowledge should be expressed in natural language, which also requires a linguistic model.

Speech Recognition

In order to design and train neural networks for tasks like audio transcription and challenging word separation and identification, DL is frequently utilized. In fact, the three primary domains of speech recognition—signal processing, phonetics, and word recognition—use these models and techniques.

Differences Between Machine Learning and NLP

NLP and machine learning are related concepts, although they are not synonymous. NLP algorithms can be seen as a specialized subset of ML.

The main difference between NLP and the more general ML is the type of data analyzed. NLP algorithms analyze, process, and interpret text-based data, while generalized ML algorithms focus more on other types of data, such as numerical data or image data.

Although NLP models are a text-oriented subset of ML, and technically they can be called ML models, the position of NLP has become so popular that few people usually refer to these linguistic algorithms as ML. At least we can say that after the Chat-GPT revolution, these algorithms have the right to be called NLP models by their pure name.

Conclusion

Now that you know the differences between Machine Learning and NLP and NLP in machine learning, you can easily understand why these two topics are so different. Machine learning focuses on creating models that learn automatically and work without human intervention. NLP, on the other hand, enables machines to understand and interpret written text.