In a world where dependency on technology is increasing day by day, our interaction with computers and machines has evolved drastically. It is seen very normally that the humans are talking to the computers in their own language and getting their work done.

But haven’t we been taught that computers only understand machine language?

Well, one of the major advancements in the field of communication with computers is the use of NLP which is Natural Language Processing that enables us humans to interact with the machines and computers in our own human language. In this blog, we will talk and explore NLP and its revolutionary applications that have changed the world of interaction with technology.

What Is NLP?

NLP stands for Natural Language Processing. Let’s break down the term in order to understand what it actually is. Natural language as in the language spoken by humans naturally and Processing means processing the human language only to get to talk with the computers. 

Now I hope it’s clear that NLP is a subset of Artificial Intelligence that is used to communicate with computers in the same natural languages as we talk to fellow humans. 

Businesses deal with a lot of unstructured, text data and require a way to process it. The language we humans use to communicate with each other makes up a large portion of the data created online and stored in databases, and organizations have been unable to efficiently evaluate this data until recently. This is one of the applications of natural language processing. 

NLP uses various AI and ML tools, and usually Python as a language along with statistical analysis and linguistic concepts such as grammatical structures and parts of speech to provide the facility of easy interaction with the computers and this is why NLP is used in various products and services nowadays. We don’t have to know complex programming languages in order to communicate with computers now as NLP has solved this problem.

How Does NLP Work?

Natural Language Processing techniques involve multiple levels that help in converting human language into a format understandable by computers. This enables machines to perform tasks that require Natural Language Understanding(NLU) and Natural Language Generation(NLG) which are the higher components of NLP.

Natural language processing techniques work with artificial intelligence to accept real-world data and interpret it into a computer-understanding format, irrespective the data is in the form of audio or text. Just like humans have ears to hear,  eyes to see and a brain to process, similarly computers have programs to read, microphones to gather audio, and software to process the data collected. The input data is translated and converted into code that the computer can interpret at some point during the processing.

Generally, languages used for NLP are Java, Python, and R . Natural language processing with Python is considered as the best choice for tasks related to NLP. This is because of its numerous libraries and tools with easy semantics that help developers perform NLP tasks faster and with ease. Another reason to choose Python over other languages for NLP is the support of integrating Python with other developing languages that help in integrating machine learning for NLP.

Here is the summary of the steps followed by NLP:

 Preprocessing of Data and Analysis:

• Tokenization: Firstly, the text is divided into smaller units, such as words or sentences, in a process called tokenization that helps in future subsequent analysis with the help of NLP using Python, Java, or R.
• Stopword Removal: Common words like “the,” “is,” and “and”  are often removed in order to reduce noise as they don’t carry any significant meanings.
• Lemmatization and Stemming: Now the text analysis of the data collected begins. Words are further simplified by reducing them to their root forms, which helps in grouping the same words that help in analyzing further.
• Part-of-Speech Tagging: NLP algorithms identify each word in a  grammatical part of speech in a sentence, categorizing them between nouns, verbs, adjectives, etc. This helps in understanding the structure of the text.
• Named Entity Recognition (NER): NER identifies and categorizes specific entities in the text, such as names of people, places, organizations, dates, and more.
• Parsing: Parsing involves analyzing the syntactic structure of a sentence, determining the relationships between words, and creating a parse tree so as to understand the grammatical structure of the data provided.

 Applying Algorithms and Models:

• Once the text is processed and analyzed, relevant features are extracted. This transformation is essential for machine learning algorithms to work with the data.
• Natural language processing relies heavily on machine learning models, such as neural networks, linguistic analysis, and statistical algorithms, to make sense of the processed text. These models help in discovering hidden patterns and relations between the data.
• Understanding the semantics, or meaning, of the text is very important in NLP. This involves capturing context, relationships between words, and the overall message conveyed by the text also known as Natural Language Understanding(NLU).
• Lastly, NLP models continuously evolve and improve through ongoing training and exposure to new data. This adaptability allows them to stay up-to-date with changing language patterns and contexts.

Applications And Uses Of NLP

Natural language processing is the major reason behind the use of machine learning tools and techniques in many real-world applications across various industries and its impact is growing rapidly. Let’s look at a few of the main areas where it is making a major contribution:

• Chatbots and Virtual Assistants: Many businesses and applications now use chatbots on their websites and messaging platforms to enhance their UI experience and provide better customer services. These chatbots use Natural language generation to answer frequently asked questions and assist with online transactions providing them with 24*7 customer support. The best of these assistants also learn to recognize contextual clues about human requests and use them to provide better and more relevant responses in their own words. Also, we all have heard about Siri, Alexa, or Google Assistant, and guess what, they all work with the help of Natural language processing and speech recognition techniques to recognize speech patterns and analyze commands given to them so that they can provide you with apt actions to what you were asking.
• Sentiment Analysis of various social platforms: You might have heard of Big data analytics and unstructured data. Data from social media platforms provide a huge amount of information which are analyzed by various organizations in order to read customer behavior allowing them to make informed and better business decisions. Natural language generation uses and is an important tool that is used in sentiment Analysis to discover hidden patterns and uncover relations by analyzing words in comments of the posts or reviews given by the customers which helps accurate analyses of the product.
• Language translation and summarization: Various language translation tools use NLP in the background to translate words of one language into another. This is not as easy as it sounds because the translating tool should provide the output without changing the context or the meaning of the input language. Various text summarization tools also use NLP to extract useful context, conclusions, and definitions in the provided big data to give an apt summary as an output.
• Spam detection: NLP plays a vital role in detecting spam emails, numbers, or messages by analyzing and detecting false grammar, misspelled names, overuse of financial terms, etc. which is usually present in spammed or phishing content.

Challenges Faced By NLP

As we realize that NLP has various applications but so are challenges in developing Natural language processing. 

• The main reason why it is difficult to develop Natural language generation is the wide range of sparse data with lots of variety, dimension, and unstructured datasets. Given these conditions, detecting and analyzing patterns in the data is very difficult.
• The development time to build an NLP tool is a lot! It takes a huge amount of time to evaluate and process millions of data points to be sufficiently trained.
• It’s very often to have ambiguities and misspellings in the data. We as humans can easily resemble the correct word or meaning of the misspelled word but as an AI tool, it is very difficult to detect or recognize the actual meaning of what’s been said or written.
• The world has various languages and in order to be globally available, we need to have diverse phrases and cultural aspects of different languages which makes the work of developing an NLP tool a lot more difficult. However, to solve this, we can assist one universal language for these purposes.
• Words and phrases with multiple meanings and expressions are also one of the challenges that NLP faces making it less accurate and far from perfect.

Since technology and its tools are advancing continuously, so are its scope and challenges which creates a lot of space for new tech champs to provide ideas to solve and overcome the challenges. Although NLP has its defaults, it still gives a lot of advantages to the world.

Conclusion

Natural Language Processing helps humans and computers to interact with each other smoothly with many forms of communication whether text or speech instructions. While Natural language generation faces a lot of challenges with data security, privacy concerns, and a better understanding of the input with a huge volume of data, it still has immense potential to work on and is an emerging technology that has the ability to get more advanced in all measures. Thus with correct and responsible use of Natural Language Processing, we can gain major benefits and even get personalized experience with the technology that replies according to our needs.