Deep dive into Natural Language Processing| How will NLP transform the world!?

4 min readAug 6, 2021

What is NLP?

Natural Language Processing(NLP)is a field of artificial intelligence (AI) that enables machines to process and interpret human languages. NLP is used to filter our inboxes, correct our spelling, and power our Internet searches. While reading this, if someone aske you where the world’s deepest lake is? The NLP in Siri comes to your rescue.

Natural Language Processing (NLP) is a tract of Artificial Intelligence and Linguistics, devoted to make computers understand the statements or words written in human languages.

Natural language processing came into existence to ease the user’s work and to satisfy the wish to communicate with the computer in natural language. Since all the users may not be well-versed in machine specific language, NLP caters those users who do not have enough time to learn new languages or get perfection in it.

In 2021, specialized security NLP solutions will arrive to help process text data more effectively. Security software companies are embedding NLP to assist in the collection, classification, extraction, and generation of text. The result will be substantial time savings in incident management, faster response times, more accurate threat detection, and better-quality data.

Getting Started !

NLP is easy to learn if you have a touch of curiosity, courage, ambition, discipline and openness.

Below is the complete roadmap on how to learn Natural Language Processing:

Prerequisites:

#Tokenization

#Stopwords

#Stemming

2. Language Modelling Techniques:

#Bag of Words

#Tf-Idf Vectorization

#Word Embeddings

3. Python Libraries:

#NLTK

#spaCy

#TensorFlow

#PyTorch

Some of the most useful computer/data science tools currently available are:

CountVectorization, Hashing Vectorization, Term Frequency-Inverse Document Frequency (TF-IDF), Lemmatization, Stemming, Parsing, and Sentiment Analysis.

Video Tutorial for NLP

Amazing Free Courses

Learn How to Get Started with Natural Language Processing | Codecademy

I hope you liked this article on how to learn natural language processing. Feel free to ask your valuable questions in the comments section below.

How NLP will transform the world?

Digital transformation in security is rapidly picking up pace as we continuously bring more processes online. We collect more data than ever, but we struggle to use it effectively. Much of our data is in the form of raw text that we don’t have time to parse and analyze — potentially leading to missed threats or undiagnosed root causes. Thankfully, recent advances in the field of Natural Language Processing (NLP) offer solutions to these problems.

1.Collection

NLP streamlines the collection of data by converting streams of information into machine-readable text. Examples of NLP-based applications for data collection include optical character recognition, voice-to-text conversion, and language translation. These are mature NLP applications, but take up within security software solutions has been slow.

2.Classification

Once we have collected more data, NLP classification algorithms can help us prioritize where to spend time. For example, sentiment analysis can differentiate between emotional states such as anger, happiness, or sadness in a block of text. Sentiment analysis is used in several social media monitoring products to carve through noisy data to identify potential threats.

3.Extraction

Narrative reports and intelligence briefs contain important data related to a threat or incident. Today, security teams manually review this raw text and record involved people, locations, and organizations during a triage stage. Named Entity Recognition (NER) algorithms can simplify this process. NER algorithms, as their name implies, recognize named entities allowing us to extract them programmatically. This automation not only saves triage time, but dramatically improves data quality.

4.Generation

One of the most visible projects in the field of NLP is Open AI’s GPT-3. This is a general AI model that takes an input set and produces narrative text afterwards. The most prevalent use of text generation today is autocomplete in your inbox. At present, the graphical user interface for this is clunky but the consistency of suggestions is uncanny.

The above paragraph was written by an AI algorithm that summarized an extended description that I fed it. It isn’t perfect, but it generated a credible summary far faster than I could have. Text generation is a fast-developing subfield within NLP with near constant innovation. There aren’t any productized security applications using text generation yet, but this is likely to quickly change. The potential first application will use an algorithm like the one I used above to condense incident and threat data into a clear summary.