Introduction to NLP

What is NLP?

So, what is NLP? NLP is the field of artificial intelligence that helps a computer to understand and interpret human language. It helps a machine to read, write and interpret linguistics just like us. However, processing and interpreting human language is no joke. The information available is very difficult and tricky to extract. This is because most of the data generated is unstructured. Think of it like this, how hard was it for you when you started learning a new language? It is the same for a machine. Fortunately, the plethora of algorithms available in NLP helps incredibly. Due to this, NLP is a hot topic and has been scaled to every aspect of our life. From healthcare to finance, NLP is everywhere.

The Evolution of Natural Language Processing

The history of NLP dates back to the 1950s when several rule-based methods were used to interpret texts for machine translation. We have come a long way since then. The concepts of Deep Learning and context-free grammar has revolutionalized NLP. They make the problem statements look considerably simple and easy to implement. The expansion of NLP peaked in the 1980s and 1990s when computational grammar and parsers were introduced to this field, fuelling the research on topics such as word sense disambiguation and named entity recognition. Now, concepts like chatbots which were once very difficult to implement, do not seem a big deal. Everybody gets blown away when a machine talks. Take the example of Apple's Siri. This software based on NLP has answers to every valid query or instruction you feed into it with great voice control. There are many more applications that we will discuss further.

Why Do We Need NLP?

NLP is in the heart of every aspect of our life, which deals with expressions. Computers were made to make our job easier. NLP contributes a lot to this. The most simple application is Machine Translation. Now, a computer can easily understand a language and translate it into another. Let us say you have a youtube channel and want to know your viewers' sentiments through their comments. This is sentiment analysis, a major application of NLP. Another is text summarization, where you can summarize a text of many words into a few. Spam detection, text classification, image captioning, speech/voice recognition, question answering, text-to-speech, the list does not end. There are plenty of concepts to explore and ideas to implement.

Top Libraries in NLP

• Natural Language Toolkit(NLTK): NLTK is the most important library for implementing the concepts of NLP in Python. One of the most important tasks you need to do in NLP projects is data preprocessing. This is where you filter the raw, unstructured text data and build a corpus for training different machine learning models. Various text processing processes, such as tokenization, stemming, lemmatization, parsing, tagging, classification, etc., can be implemented using NLTK. It also has 50 corpora and lexical assets.
• Scikit Learn: If you are into machine learning, you must have encountered the open-source Scikit Learn library. It is very common among NLP applications. It is mainly used in NLP for text pre-processing and POS tagging. The only drawback of this library is that it does not use Deep Learning algorithms in its classes and packages. Nevertheless, Scikit Learn is widely used in applications of a bag of words and tf-idf with the help of functions like TfidfTransformer.

• TextBlob: This library is powered by NLTK and was created to improve the efficiency of the NLP models. It is the fastest library among the rest and is user-friendly, especially for beginners. With TextBlob, you can perform several NLP tasks, such as Parts Of Speech tagging, sentiment analysis, tokenization, classification, etc.
• SpaCy: Another open-source NLP library based on the programming language Cython. This library is known for its speed and efficiency. In addition, it has tokenization functionality with linguistics of 49 different languages and provides multi-trained transformers such as BERT.

A Variety of Real-World Applications Using Nlp Functions

NLP is widely used in many real-world applications. Some of them are listed below:-

• Monitoring Social Media: Life without social media is unimaginable. Through it, there is a lot of information and data flowing around. These can be extracted, processed, and channeled for a novel purpose. Business decisions can be supported, and we can analyze the sentiments of the target audience for a product.
• Language Translation: Open the Google app on your smartphone and ask it to translate an English sentence to Spanish. It will be done instantly. The underlying concept here is Machine Translation, where we process a text written in one language and change it to another while keeping the semantics intact.
• Chatbots: Chatbots are one of the best ways to keep customers satisfied with a product for a company. This NLP tool is available 24 hours for your queries. This technology replaces call center jobs and human-to-human customer care, which is very tedious. In addition, it stops the frustration of customers waiting to contact a company's authorities to resolve a problem.
• Voice Assistants: Have you ever used Alexa or Siri? Aren't you amazed how you have to ask a device to pay for a song, and it's played? These are all wonders of Natural Language Processing. A voice assistant performs actions according to certain verbal instructions by combining speech recognition and natural language processing. It is a powerful and reliable upgrade over chatbots and has surely scaled up our living standards.
• Grammar and Spelling Checker: This application of NLP is widely used and has helped a large part of our society. Now, you don't have to worry about your mistakes while writing an article or a speech. Instead, the corrections are done in real-time, and you get a grammatically correct piece of text with a better choice of words and phrases.

Advantages of NLP

• Cost reduction and smooth processing: NLP applications can do a job that requires days in seconds, such as data analysis. It can easily do the job of multiple hands with better efficiency.
• Accurate and Trustworthy: Human error is common in any task, especially if you must repeatedly do it. NLP tools can adapt to your business's requirements and do the same task accurately. Moreover, you can be sure of the results produced.
• Improved Customer Service: The best example is the chatbots that take in your request or query, sort it according to a given priority, and channel it to the concerned authorities. This saves much time for the customer making him/her satisfied with the product.
• Raises Living Standards: Imagine you walk into your home, tired, and you just have to say out a few instructions, such as play music, turn on the lights, etc., and it happens right away. NLP contributes a lot to our day-to-day life.

Disadvantages of NLP

• Large Training Time: Our NLP models must be highly accurate and efficient. However, to achieve this, our model should be supported by a large amount of data which would take a significant amount of time to be processed and trained.
• Reliability: No machine learning model has 100% accuracy. We cannot fully depend on them. There is always a chance of getting some errors in the predictions and results of the model.
• Limited functionality: Since most of the results and predictions of an NLP model are based on data, its functionality is limited. The model will fail when subjected to new domains as it is built for a specific task.
• Low resources: The less popular languages have very few resources, hence becoming difficult to work with. The cornerstone of any machine learning algorithm is data; without it, things become strenuous.