What is NLP: Steps in Training Models & Some Popular Applications

The fantasy of having machines talk and respond to us in a human-like manner is already a reality. This keeps getting more and more realistic with every passing day. With chatbots answering your queries on websites and ChatGPT generating a crisp piece of information, it has come a long way.

But, there’s one thing that you must notice here. None of them are human. How do they manage to sound and seem so human? How do they respond in such an intelligent manner, and how are they so articulate? This is because of natural language processing.

Natural language processing or NLP is a branch of artificial intelligence, which gives machines the ability to read and understand human languages. It is a set of algorithms that helps computers make sense of textual data.

In this article, we will discuss the following:

• What is NLP?
• What are the steps in NLP training?
• What are the applications of NLP?

What is NLP?

NLP is a branch of artificial intelligence that combines the field of linguistics and computer science. It deciphers language structure, patterns, and rules to help develop models which can comprehend text and speech.

In simple words, NLP is a technique that breaks down and separates significant details from text and speech, helping computers communicate with humans.

What are the steps in NLP training?

NLP algorithms train on vast amounts of data, which might be in the form of text or speech. The goal of NLP is to enable computers to process and understand human language in a way that is like how humans do it.

But, to make an algorithm understand text data, you need to transform it into a form that computers can understand.

So, training NLP models is no different than making a child learn to read for the first time. It is a five-stage process:

1. Segmentation
2. Tokenizing
3. Stemming/Lemmatization
4. POS Tagging
5. Named Entity Tagging

Segmentation

Segmentation involves breaking down the entire document into its constituent sentences. You can do this by segmenting the article along its punctuation like full stops and commas.

Tokenizing

We break down our sentences into their constituent words and store them. Each word is a token. We can make the learning process faster by getting rid of non-essential words like punctuation, special characters, stop words, etc. These are words that do not add much meaning to the sentences, but make sentences sound more cohesive.

Stemming/Lemmatization

Both stemming and lemmatization are text pre-processing techniques.

Stemming is the process by which we explain the model that some words like ‘go’, ‘going’, and ‘gone’ are the same word, ‘go’. It is one word with added prefixes and suffixes. It is just like cutting down the branches of a tree to its stems. For example, the stem of the words ‘eating’, ‘eats’, and ‘eaten’ is ‘eat’.

Lemmatization is the process of grouping different forms of the same word. The models break a word down to its root meaning to identify similarities. For example, a lemmatization of the word ‘better’ or the word ‘best’ is ‘good’. It reduces the word to its dictionary form.

POS Tagging

POS stands for Parts of Speech. The NLP models use POS tagging to learn nouns, verbs, articles, and other parts of speech in the input text. In POS tagging, there is an addition of the appropriate tags to our words. For example, the word ‘Bill Gates’ would have a tag of ‘noun’, while ‘running’ will have a tag of ‘verb’.

Named Entity Tagging

Named Entity Tagging refers to the process by which the models identify entities in a text. These can be organizations, personalities, locations, etc. mentioned in the document. For example, ‘Bill Gates’ would be tagged as a ‘personalities’ and ‘Microsoft’ as an ‘organizations’.

What are the applications of NLP?

The evolution of NLP and overall artificial intelligence has the potential to revolutionize human interaction with computers. As of today, the most popular NLP applications include sentiment analysis, chatbots, and text summarization. Other applications are virtual assistants, text-based and speech-based search engines, language translations, etc.

Sentiment Analysis

Sentiment analysis is one of the most popular applications of NLP. It involves the use of algorithms to identify, extract, and quantify the sentiment or emotion expressed in each text.

Sentiment analysis can be performed on a wide range of texts. This includes social media posts, customer reviews, news articles, and more. NLP algorithms analyze these texts to determine whether the sentiment expressed is positive, negative, or neutral.

Sentiment analysis has many applications across different industries. For example, in the field of retail, sentiment analysis can gauge public opinion for a particular product or service. The analysis identifies the most used phrases or keywords associated with a brand. Based on it, the sentiment can be evaluated.

In the financial industry, sentiment analysis can track public sentiment for a stock or company. The recent news articles, announcements, press releases, and social media discussions are analyzed to find the sentiment. Based on it, people make investment decisions.

Chatbots

NLP is an essential technology for building chatbots, which can communicate with humans naturally and intuitively. By using NLP models, state-of-the-art chatbots are brought to life. They can then understand the intent and context as well as the sentiment of the input given to them and respond appropriately.

Natural Language Understanding (NLU): NLU is part of NLP that helps chatbots understand the meaning of human language. NLU algorithms can parse user input and extract relevant information, such as intent, entities, and context. This helps chatbots provide more personalized and accurate responses.

Natural Language Generation (NLG): NLG is part of NLP that helps chatbots generate natural-sounding responses. These responses are what a human would say. NLG algorithms use templates and rules to create responses based on the information provided by the NLU.

Language Translation

NLP techniques can translate text from one language to another automatically. It goes beyond simple word-to-word translation and doesn’t have any human involvement. The translation preserves the meaning, context, intent, and sentiment of the input while producing a fluent output.

It is pretty useful to provide information to people in foreign countries who don’t know or speak the native language. Besides this, it is employed to translate textbooks, documents, and research papers online for global audiences.

Text Summarization

Text summarization involves generating a summary of a longer text. NLP models can analyze the structure of a text and identify its most important parts. It means identifying the most important sentences, phrases, or keywords in a text. Based on it, they extract the most important sentences from a text and use them to create a summary.

Another approach is to shorten long sentences in the text by carrying their main idea. The model understands the meanings of the sentences to generate fresh sentences that convey their crux of them. The final summary contains the most important facts from the original document.

Text summarization is particularly useful for applications such as news articles and research papers. Also, for legal documents where it is often necessary to quickly understand the main points of a text.

Search Results

Search engines that take text or voice queries as input to generate a list of search results also employ NLP algorithms. They understand a searcher’s natural language query, the intent, the sentiment, and the context around it. Based on it, they produce search results that are most relevant to the searcher.

NLP-powered search results are also important for e-commerce sites, education portals, social media platforms, etc. Wherever users intend to find relevant information and search option is available, NLP algorithms can enhance search results.

Email Filtering

Everyone has come across email classification, which is achieved with the help of NLP algorithms. Some of the emails get transferred into the spam section, while other emails get transferred to the personal or promotions section automatically.

It reduces the amount of irrelevant or unwanted emails that reach a user’s inbox. It improves email security by reducing the risk of phishing attacks and malicious emails. Overall, it helps the email receiver focus on emails that are important to him/her.

Virtual Assistants

Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortana, and Google Assistant – are all powered by NLP. By employing NLP algorithms, they receive, understand, and respond to voice commands from users. That too in a much human-like way.

The NLP algorithms analyze the user’s spoken words and identify the content as well as the intent behind the request. Based on its understanding, the algorithm retrieves relevant information from a database or web search to provide a response. In this way, they can handle complex queries and improve their responses over time by learning from user interactions.

As NLP technology is progressing, virtual assistants are evolving and getting smarter. They can now understand variations in language and syntax, recognize the mood of the user, perform tasks such as switching off lights & making tea (devices operated via Wi-Fi), etc.

Product Recommendations

NLP is widely used for the analysis of customer reviews and search queries in the e-commerce & retail industry. It helps in identifying patterns in customer behavior and preferences. This information helps in providing personalized recommendations to customers. It enhances customer experience, leading to increased customer satisfaction and sales for businesses.

Generating Content

NLP involves training algorithms to understand the structure and patterns of language. It gauges the features and characteristics of the textual data. This knowledge helps to generate new content such as product descriptions, news articles, and entire books.

However, it requires large amounts of text data for training the models. Then, the model generates unique, coherent, and meaningful sentences and paragraphs. In this way, it can streamline content creation and reduce the workload for human writers.

But, there are some ethical concerns about the use of machine-generated content. For example, NLP algorithms can be biased toward a topic, there are ambiguities over the authorship and copyright of the generated content, the information can deceive or manipulate people, etc.

Text Prediction

Using NLP algorithms, we can suggest the next word or phrase in a sentence. When we train the model, it learns the context and patterns of language. It also identifies common sequences of words and the likelihood of certain words following others. All this helps it to predict forthcoming words, phrases, and sentences.

This technology is useful in text messaging apps, virtual keyboards, and autocomplete features. It can improve typing speed and accuracy by suggesting the next word before it gets typed.

Speech Recognition

Speech Recognition is a very important application of NLP. It involves analyzing spoken language and transcribing it into written text. The algorithms analyze patterns in speech such as intonation, pitch, and frequency.

Then it matches them to words and phrases in the respective language model. Based on the matches, it produces the output. With advances in NLP technology, speech recognition has become more accurate and reliable.

Speech recognition is widely used in powering virtual assistants, transcription services, and language translation. It is also used for voice searches, voice biometrics, customer services, pre-sales communication, etc.

End Note

Natural Language Processing is a set of techniques that help machines understand the literal meaning of sentences. It also helps them recognize the sentiments, tone, opinions, thoughts, and other components that are important for proper communication.

While there are many applications of NLP at present, the future would allow us to interact with machines even better. The NLP market size was around $16 billion in 2022. It is expected to reach approximately $50 billion in 2027, representing an average annual growth rate of over 25%.

So, with enhanced data availability and quality, NLP algorithms will become more powerful. There will be more stronger and universal ethical safeguards in place to prevent the misuse of NLP technology.

With that said, conversational AI tools will be smarter, voice biometrics will be in place, and much more will happen. Just wait and watch!

If you have any requirements for NLP projects, contact us today. We’d love to hear from you.