Humans interact with each other through written text and verbal speech. All of these are made up of some sort of language. This is known as Natural Language. Computers and machines are now a quintessential part of our lives as we do many of our daily chores using them. Thus it is now very important for us to communicate with computers.
This is not an easy job since computers understand machine language. It is very difficult for humans to interpret the meaning of numerous 1s and 0s. That is why in the 1950s, the concept of automatic translation started emerging. With the advancement of technology, Natural Language Processing (NLP) was introduced.
Natural Language Processing (NLP) is a component of Artificial Intelligence in the field of linguistics that helps a computer to interpret or manipulate the natural language written or spoken by humans, with the help of software.
NLP works by combining Machine Learning, Deep Learning and statistical models, and due to the vast availability of Big data, Powerful devices and algorithms, it is one of the fastest advancing technologies.
Statistical Analysis combines Machine Learning models and Deep Learning models along with Computer Algorithms to extract and differentiate the text and voice data and statistically provide meaning to all the elements.
Let us learn briefly about Natural Language toolkits, Importance of NLP and its applications in this article.
Natural Language Toolkit (NLTK):
Natural Language Toolkit is a platform for building algorithms using Python Programming Language to manipulate natural language. Human-readable text is mostly unstructured data for computers. With the help of NLTK, these texts can be preprocessed before analyzing.
NLTK also provides user-friendly interfaces across 50 corpora and linguistics sources, for example, WordNet, text processing libraries for classification, tokenization, and wrappers for industrial-strength NLP libraries.
Some of the features of NLTK according to Real Python (source) are:
Tokenizing: It is used to segregate any text by word or by sentence. This allows the user to work with small pieces of coherent texts that have some sort of meaning outside the context of the rest of the text.
Filtering Stop Words: It is used to ignore stop words while processing any text. Common words like in, is, an, etc., are often stop words as they don’t add meaning to the text.
Stemming: It is a text processing task that reduces any word to its root word. It helps the computer to understand the meaning of the word, rather than how it is used in the sentence.
Chunking: Chunking is similar to tokenizing, but it is used to identify phrases instead of words or sentences.
Chinking: Chinking is used along with Chunking. But chinking is used to exclude a pattern, whereas chunking is used to include a pattern.
Tagging Parts of Speech: Tagging is a process by which different words in a sentence are labelled according to their parts of speech.
Name Entity Recognition (NER): It is used to locate named entities in your text and determine what type of named entity they are.
These are some of the features. There are many other things that can be done with the help of NLTK. NLTK are very convenient to use as they are easy to implement and consistent. It’s target audience consists of both linguists and computer specialists and it is suitable for jobs like translation, education, research, industrial applications, etc.
(Must read: Natural Language Toolkit)
Applications of NLP
NLP helps in a vast range of services starting from Business Analytics, Speech recognition, Social media etc. Let us discuss some of them:
Chatbots and Voice Assistants:
We use a lot of chatbots and voice assistants in our daily life, the popular ones being Alexa, Siri, Google Assistant, etc. Other than these, most websites inculcate these virtual assistants to assist the users and respond to their queries 24/7. These are programmed with the help of NLP and speech recognition to understand the text and voice commands of the user.
Email Filtering is one of the most helpful uses of NLP in today’s world, as most of the professional work is done through Emails. This is done with the help of text classification, where all the emails received are separated into different sections. Gmail separates all the received mails into primary, social, promotional and spam sections. So, it is easier to find the important emails.
(Recommended read: 7 Natural Language Processing Techniques for Extracting Information)
NLP has helped a lot in the field of Business. One of the features that has been developed with the help of NLP is Targeted Advertising. It is a type of online advertising where any internet user gets an advertisement on the basis of their online activity.
This is very beneficial for businesses as it saves them a lot of money, since the ads are only shown to customers who are actually interested in the service/product.
With the help of Sentiment Analysis, the expressions in a sentence can be identified. Human speech could be very hard for the computer to understand as it involves various expressions beyond literal meanings.
Natural Language Understanding (NLU) is a subfield of NLP that helps the machine to catch on to different sentiments that might be implied in a sentence.
Auto-correction and auto-prediction are features that we use on a daily basis, and is one of the most widely used applications of NLP. These softwares include several functions such as proposing synonyms, correcting grammar and typos, rephrasing phrases, and providing clarity to the text, and may even anticipate the tone of the statement that the user may have suggested.
Speech Recognition is a computer technique that converts speech input data to a machine-readable format. Speech recognition is utilised in a variety of sectors, including virtual assistants, speech-to-text, interpreting speech, and sending emails.
NLP helps the HR team of any company by sifting through all resumes and shortlisting applicants using various approaches such as information extraction and name entity recognition. It considers many factors like location, talents, education, and so on, and picks applicants that closely match the company's requirements.
Advantages of NLP
Every Internet user has experienced NLP in some way or another. Following are the advantages of Natural Language Processing:
It helps improve customer experience with the help of chatbots and virtual assistants that provide help to the customer. It also helps to recognize the sentiments of any customer through their review.
Through NLP, businesses and organisations can understand market trends and their customer base with social media analytics, as social media generally plays a vital role in the growth of any business.
It minimizes many manual repetitive tasks by automating them, like information extraction etc. that gives the employees independence to work autonomously. Hence increasing employee satisfaction and engagement.
It reduces the investment of businesses with processes like Targeted marketing. It gives the companies liberty to invest the amount somewhere else, hence expanding the business.
It helps businesses to get foreign customers as communication has become easier through different translation tools that are developed with the use of NLP.
(Read also: Examples of NLP)
Disadvantages of Natural Language Processing
Like there are many advantages, there are also a few disadvantages of NLP:
Computers are still unable to identify and process poorly worded or ambiguous statements. This might lead to inaccurate results.
Due to limited functions, the systems are stuck with specific tasks and are unable to expand their domain.
The main NLP system does not have any user interface.
NLP is rapidly advancing everyday due to improvement in the field of AI. There are a lot of applications of NLP which benefit individuals and businesses everyday. But, like advantages, NLP has some disadvantages as well, that are discussed above. There are a lot of subfields of NLP, like semantic analysis, speech recognition and NLU, that has helped in making communication easier.