Top 8 NLP Techniques Data Scientists Must Master

Artificial intelligence (AI) aims to build machines that mimic human intellect and behavior. The fact that humans are constantly attempting to incorporate languages into machines and software through the field of artificial intelligence should, therefore, not come as a surprise to anyone. They are carrying out this activity using a method known as natural language processing (NLP). To learn more about AI and other AI techniques, refer to the data science course in Pune, which provides hands-on experience with its live projects. 

What is NLP?

NLP, or natural language processing, is the AI-driven process of making human language input understandable and understandable to software and computers. Fundamentally, NLP comprises two processes: natural language generation and natural language understanding 

  1. Natural Language Understanding (NLU) – Describes methods designed to work with a language’s syntactical framework and extract semantic meaning from it. Named Entity Recognition, Speech Recognition, and Text Classification are examples.  

  1. Natural phrase Generation (NLG) is an advancement in phrase generation over Natural Language Understanding (NLU). Examples include the creation of text, answering questions, and speech.  

Now let’s examine the top NLP Techniques  

  1. Tokenization  

One of the most fundamental and important NLP methods is tokenization. Breaking down a long text string into smaller units is an essential stage in text processing for an NLP application. A token is a unit that stands in for a phrase, symbol, number, etc.   

When creating NLP models, these symbols make it easier to comprehend the context. They serve as a model’s building components as a result. Tokenizers frequently use a blank space as a separator between characters. Depending on your objective, the following tokenization methods are used in NLP:  

  • Tokenization of White Space  

  • Based on rules, tokenization  

  • Dictionary-based Tokenization with Spacy  

  • Tokenization of subwords  

  • Using Penn Tree Tokens

  1. Lemmatization and stemming  

The next most crucial NLP method in the preprocessing stage is stemming or lemmatization. It alludes to stripping a term down to its root, connected to a prefix or suffix. Lemmatization is a text normalization method that converts all words to their base root mode.   

Chatbots and search algorithms use these two methods to comprehend the meaning of words. Both methods attempt to discover the word’s origin. Lemmatization is more sophisticated than stemming in that it produces the core word through morphological analysis, as opposed to stemming, which concentrates on removing a word’s prefix or suffix.  


  1. Stop Words Elimination  

Following stemming and lemmatization in the preprocessing portion is stop word removal. Many terms in a language are merely fillers; they have no inherent meaning. Conjunctions like since, and, because, etc., are prime examples of this. Fillers include prepositions like in, at, on, above, etc.   

Such terms are not useful in an NLP model in any meaningful way. It is not necessary to halt word removal for every model, though. The choice is based on the nature of the job. For instance, stop word removal is a useful method for text categorization. However, stopping word removal is unnecessary for computer translation or text summarization.   

  1. Keyword Extraction

People who read a lot naturally improve their skimming abilities. They skim through a text, whether it be a book, magazine, or newspaper, keeping only the important words and leaving out the rest. As a result, they can quickly and easily determine a text’s significance.  

Using NLP techniques, keyword extraction accomplishes the same task by identifying the crucial words within a text. As a result, keyword extraction is a text analysis method that produces insightful information for any subject. As a result, reading a paper doesn’t require much time. To obtain pertinent keywords, you can just use the keyword extraction method.  

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  1. Word Embeddings

How to translate a body of text into numerical values that can be given to machine learning and deep learning algorithms is a crucial problem that NLP data scientists must solve. To address this problem, data scientists use word embeddings, also called word vectors.  

Word embeddings are a method of representing text and papers with numerical vectors. In a smaller-dimensional space, it depicts individual words as real-valued vectors. The representations of related words are comparable. In other words, it is a technique that allows us to extract textual characteristics for inclusion in machine learning models. Word embeddings are therefore required to teach a machine learning model.  

You can use predefined word embeddings or learn them from fresh for a dataset. Word embeddings can be found today in various formats, including GloVe, TF-IDF, Word2Vec, BERT, ELMO, CountVectorizer, etc.  

  1. Sentiment Evaluation

An NLP method called sentiment analysis is used to contextualize a text and determine whether it is positive, negative, or neutral. It is also referred to as edge AI and opinion mining. This NLP method is used by businesses to categorize text and ascertain customer sentiment regarding their product or service.  

Social media platforms like Facebook and Twitter frequently employ it to stifle hate speech and other objectionable material.  

  1. Topic Modeling 

In natural language processing, a topic model is a statistical model used to extract hidden or abstract subjects from various documents. Because it is an unsupervised machine learning method, training is not required. Additionally, it enables data analysis to be simple and quick.   

Companies use topic modeling to discover recurring words and patterns in customer reviews to pinpoint topics. Therefore, topic modeling allows you to rapidly identify the most important topics from a large amount of customer feedback data rather than spending hours doing so. This allows companies to deliver better customer service and enhance the perception of their brands.  

  1. Summary of Text  

Using the text summarization method of NLP, a text can be condensed and concise while remaining coherent and fluid. It allows you to get the most crucial information out of a document without pursuing it word for word. In other words, this automated summary saves a ton of time.  

There are two methods for text summary.  

  • Extraction-based summarization: This method doesn’t involve altering the source material. Instead, it merely takes a few words and sentences from the text.  

  • Abstraction-based summarization: This method extracts the key ideas from the source text and transforms them into new phrases and sentences. It paraphrases the source text, changing the way sentences are put together. Additionally, it uses AI tools to handle any grammatical or consistency issues brought on by the extraction-based summarization method.


All-natural language processing apps based on AI employ NLP techniques like tokenization, stemming, lemmatization, and stop word removal. They come under the preprocessing category. Similarly, text analysis tools like text summarization, TF-IDF, and phrase extraction are useful. But these methods also form the basis for NLP model training. If you are curious to learn how NLP techniques work, visit the data science and data analytics courses offered by Learnbay and become certified  by IBM. 



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