5 NLP Techniques That Data Scientists Must Know

Byrihangowda

Natural language processing is an artificial intelligence subfield that aims to make machines understand natural languages in the same way humans do. The Turing Test (also known as the Imitation Game) was developed in the 1950s to determine whether a machine can be considered intelligent.”

 

The Turing test is a watershed moment in artificial intelligence research and development. According to it, if a human cannot tell whether they are speaking to a machine or a person during a conversation, the Turing test has been passed, and ultimate machine intelligence has been achieved. Although scientists are still debating whether a machine has passed the Turing test, there are many exciting applications of NLP in business.

 

Gmail can autocomplete your mail as you type, LinkedIn can provide response recommendations to a text message, and Google’s search engine auto-fills the search query for you and returns the most relevant results, not to mention the Virtual Assistants, Siri and Alexa, who can converse as naturally as a human. OpenAIs GPT-3, the most powerful and most significant artificial intelligence model trained on 45TB of data and processed through 175 billion parameters, can produce language that is both stunning and unsettling. 

 

Here are the top 5 NLP techniques, every data scientist must know:

  • Stop Words Removal

 

Stop word removal is the preprocessing procedure that follows stemming or lemmatization. Many words in any language are essentially fillers with no meaning linked to them. These are generally words that are used to connect sentences (conjunctions- “because,” “and,” “since”) or to illustrate a word’s relationship to other words (prepositions- “under,” “above,” “in,” “at”). These words constitute most of the human language and are not particularly useful when constructing an NLP model. However, because it depends on the objective, stop word removal is not a traditional NLP approach to incorporate for every model. 

 

When doing text classification, for example, if the text needs to be classified into different categories (genre classification, spam filtering, auto tag generation), then removing stop words from the text is beneficial because the model can focus on words that define the meaning of the text in the dataset. For detailed explanation, refer to the IBM-accredited Data Science Course in Delhi, which is trending in the market. 

  • TF-IDF

A statistical method called TF-IDF is used to assess a word’s significance within a group of documents. The TF-IDF statistical measure is computed by multiplying two distinct values: the term frequency and the inverse document frequency.

The idea behind TF-IDF is to find essential words in a document by looking for words frequently occurring in that document but not elsewhere in the corpus. These words could be computational, data, processor, etc., in a computer science document, but extraterrestrial, galactic, black hole, etc., in an astronomical document. Now, let’s look at an example of the TF-IDF NLP technique using Python’s Scikit-learn library.

  • Extraction of Keywords

When you read a piece of text, whether it’s on your phone, a newspaper, or a book, you do this involuntary action of skimming through it- you generally skip filler words and pick significant terms from the text, and everything else fits in context. Keyword Extraction performs the same function as locating essential keywords in a document. Keyword Extraction is a text analysis NLP tool for quickly gaining valuable insights on a topic. Rather than going through the entire manuscript, the keyword extraction technique can be utilized to condense the content and extract relevant terms.

 

The keyword Extraction technique is beneficial in NLP applications where a company wants to identify customer problems based on reviews or if you’re going to identify topics of interest from a recent news item.

  • Embeddings of Words

Given that machine learning and deep learning algorithms only accept numeric input, how can we turn a block of text into numbers that these models can use? When training any model on text data, whether classification or regression, it is necessary to convert it to a numerical representation. The straightforward solution is to use the word embedding approach to representing text data. Using this NLP method, you can express words with related meanings in a similar way.

  • Sentiment Analysis

The emotional analysis is another name for sentiment analysis. One of the essential NLP tools for text classification is AI, often known as opinion mining. The goal is to categorize text such as a tweet, news article, movie review, or any text on the internet into one of three categories: positive, negative, or neutral. Sentiment Analysis is most commonly used to reduce hate speech on social media platforms and identify distressed customers in negative reviews.

 

These were the 5 popular NLP techniques every budding data scientist should master. Furthermore, if you’re considering a career in data science and AI, take a look at the top Data Science Certification Course in Delhi. Enroll and explore the top ML techniques to gain a competitive edge. 

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