What is Natural Language Processing?

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While NLP has been around since the 1950s, the field was revived during the past few years due to the increasing use of the Internet, which allows communication in natural languages, and the development of large-scale data repositories, which are available for linguistic research.

NLP stands for Natural Language Processing. It is an interdisciplinary field that studies human language and its relation to computers. It can be considered as a subfield of artificial intelligence, computational linguistics, cognitive science, information retrieval, text mining, and computational semantics. Let’s see more about NLP, its uses, and different tools.

Define Natural Language Processing

Natural language processing (NLP) is defined as the task of identifying and extracting information from natural language, typically spoken or written. In its most simple form, NLP is a means to automate linguistic processing tasks such as spelling correction, part of speech tagging, and text summarization. The process is relatively straightforward and involves identifying the semantic meaning of a given sequence of words, using the context provided by the preceding input. 

Current NLP technologies can be divided into three broad categories: syntactic parsing, semantic processing, and statistical approaches.

  • Syntactic parsing is the process of analyzing language using grammatical rules. It is commonly used in NLP (natural language processing) but can be applied to any kind of analysis of text, such as speech recognition.
  • Semantic processing means understanding the words of a sentence and the grammar of a language. With NLP, this means understanding the concepts contained within a sentence and applying the appropriate meaning to those concepts.
  • Statistical approaches are used in Natural Language Processing (NLP) to help process large amounts of data. The statistical approach relies on statistics and math to provide a basis for understanding the language and meaning behind the data. It can be used to help identify trends and commonalities within data. It can also help predict future behavior and outcomes based on historical data.

What is Real Time Natural Language Processing

The real-time NLP API enables users to do real-time text analysis and get useful insights into the documents they receive, without having to build and deploy custom analytic engines. It includes several advanced features not found in other APIs. It enables users to leverage existing text analytics resources for the collection and also analyze documents without adding them to the index. With this, users can check the results of the analysis without having to wait for the index to build or for it to be updated. The SIAPI version only accepts content in text format, while the NLP RESTAPI accepts both text and binary content.

It might take additional time to send a call to the real-time NLP API that needs to initialize a document processor. You must have an initial index and your analytic resources are already deployed for initializing the Document processor. 

The document processor’s resources are shared with both real-time NLP API requests and normal document processing. The performance of the real-time NLP performance could be affected by the creation of an index and vice-versa.

What are the Uses of NLP

When it comes to NLP, there are many use cases, especially in business. 

In finance, analyzing financial statements to find trends and patterns. This is one of the keys uses for NLP, especially in data analysis. An analyst might analyze a company’s financial statements to find trends and patterns. This could include looking for changes in the company’s income, assets, and liabilities, as well as looking for correlations between different variables. This information can help the analyst make educated guesses about the company’s future performance.

In marketing and SEO, finding keywords that are both highly searched and relevant to a particular product or service. For example, if you sell books, you could search for the words “How to Start a Small Business” in Google and see what comes up. Chances are, the first three or four pages of results will be relevant to your business.

In healthcare, finding meaningful relationships between symptoms and medical conditions to better diagnose patients. This is a perfect use case for text analytics (i.e., using NLP). A text analytics tool could be used to connect symptoms with medical conditions to better diagnose patients. This could help narrow down which patients may require further testing or treatment.

In content creation, identifying topics and creating content based on what people are searching for. For example, if you own a fitness center, you can search for “exercise” and find out what people are searching for in this area. 

In recruiting, finding the right job candidates. This is another great example of using NLP. If you are a job recruiter, you can analyze job postings and recruiters to better understand what employers are looking for. What qualities do employers look for in candidates? Some qualities that employers may look for are intelligence, creativity, good communication skills, and a strong work ethic.

In sales, determining what the best next steps are for a prospective client. For a prospective client, determining what the best next steps are can depend on a variety of factors, including the client’s needs and goals, the salesperson’s skill set, and the availability of resources. 

What are Various NLP tools

Python and the Natural Language Toolkit (NLTK)

With the recent release of version 3.0 of the open source Natural Language Toolkit (NLTK), it’s possible to implement natural language processing into your Python programs.

NLTK allows you to analyze large amounts of text without having to install other programs. It uses statistical techniques to identify and extract useful information from text. There are a lot of things you can do with natural language processing, including text mining, sentiment analysis, machine translation, speech recognition, and even automated writing. It can identify parts of speech and then perform stemming. It can also detect the main topic, the author of the document, and the date. 

These things are very useful in many different applications. People use natural language processing tools to perform tasks such as identifying spam messages or filtering information based on how it is written. This is an area of technology that is rapidly developing, so you may find yourself using natural language processing more and more in the future.

Statistical NLP, Machine Learning, and Deep Learning

The earliest NLP applications used hand-coding, rules-based systems that could perform specific NLP tasks. Hand coding these systems is time-consuming and requires expert knowledge and experience in NLP, and can be challenging to scale.

Statistical NLP along with machine learning and deep learning automatically extracts, classifies, and labels elements of text and voice data. Then, it assigns a likelihood to each possible meaning of those elements.

In the last few years, deep learning systems have made it possible for machines to “read” text in ways humans couldn’t even dream of, and can now translate, write, transcribe, read and understand voice, convert speech into text and perform other functions with far greater accuracy than human performance.

Natural Language Processing with Python

Following are some of the NLP techniques that can be done by using Python:

Sentiment Analysis

Sentiment analysis uses a simple and effective tool for understanding people’s feelings about certain topics. It measures how positive or negative an item is about another. There are many ways that this type of analysis can be applied to get some helpful information about people or businesses. It is applied to healthcare, customer service, banking industries, etc.

Name Entity Recognition

Named Entity Recognition is an NLP technique for locating and classifying named entities in text such as persons, organizations, locations, expressions of times, quantities, monetary values, percentages, and other types of expressions. It can be used for a variety of tasks, such as search engine optimization, recommendation systems, customer support, content classification, etc.


Wordcloud is a popular way to identify the most important keywords in a document. In a word cloud, common words have a bigger and bolder font, while uncommon words have a smaller font. You can create beautiful word clouds using the style cloud library or the word cloud library in Python.

Bag of Words

A bag of Words is a representation that takes text documents and turns them into fixed-length vectors that helps to represent text into numbers. So that it can be used for machine learning models. 

The Bag of Words model does not care about word order. It is only concerned with the frequency of words in the text. Apart from NLP, these types of models are applied to retrieve information from documents and classify those documents.

Term Frequency–Inverse Document Frequency (TF-IDF)

The TF-IDF algorithm takes into account the importance of words in the text. The TF (Term Frequency) value is used to determine how often a term occurs in a text. The IDF (Inverse Document Frequency) value gives the weighting of a term to the other terms in the document. This way, very common terms are given less weight than rare terms. As a result, the algorithm can give more importance to rare terms. This is also known as inverse document frequency.

Closing Words

Your users will always somehow use NLP in some way. One question then remains is: is your company, along with your AI systems, prepared to use it?

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