What is NLP - Detailed Guide to Natural Language Processing

Have you ever wondered how virtual assistants like Alexa, Siri, or Cortana understand what you're saying and respond intelligently? It may appear to be magic or something, but it is not. All of this is made possible by a technology known as NLP. 

NLP refers to Natural Language Processing. It involves a range of tasks, including speech recognition, natural language understanding, natural language generation, and machine translation. These tasks are accomplished using various techniques such as machine learning, deep learning, and statistical modelling.

To pursue a career in this field and gain a degree, you can apply for a BCA Program. While NLP is not typically a specialisation within a bachelor’s in computer application, BCA students can gain knowledge and skills related to NLP through their coursework and electives.

Some BCA Courses may offer specialisations in artificial intelligence, machine learning, and natural language processing, which can provide a foundational understanding of NLP concepts and the top new technological trends. 

What is NLP? 

Natural Language Processing (NLP) is a field of computer science and artificial intelligence that focuses on the interaction between computers and humans using natural language. Normal computers cannot understand this natural language as they communicate through binary codes. 

It involves teaching computers to understand, interpret, and generate human languages, such as text and speech. NLP techniques use algorithms, statistical models, and machine learning to analyse and process large amounts of natural language data. 

Some real-world applications of NLP include chatbots, virtual assistants, speech recognition, machine translation, and sentiment analysis. In simple words, NLP helps computers understand and process human language so they can communicate with us in a more natural and human-like way.

How Does NLP Work?

Performing NLP typically involves the following steps:

1. Data collection- The first step involves gathering text or speech data that will be analysed and processed using NLP techniques. The data can be obtained from various sources, such as websites, social media, audio recordings, or written documents.

2. Text preprocessing- This step involves cleaning and normalising the text data, such as removing punctuation, stop words, and other irrelevant information. This step is important to ensure the quality and consistency of the data.

3. Tokenisation- Tokenisation involves breaking text data into individual words or phrases, also known as tokens. This step helps to simplify the text data and makes it easier to analyse and process.

4. Part-of-speech (POS) tagging- POS tagging involves identifying the parts of speech of each word in the text data, such as nouns, verbs, and adjectives. This step is important to understand the grammatical structure of the text and to identify the relationships between different words.

5. Named Entity Recognition (NER)- NER involves identifying and extracting named entities, such as names of people, organisations, locations, and products, from the text data. This step is important for many NLP applications, such as information extraction and sentiment analysis.

6. Sentiment Analysis- This involves analysing the tone, attitude, and emotion expressed in the text data. This step is often used to gauge the opinion of the writer or speaker and can be used in applications such as social media monitoring and customer feedback analysis.

7. Machine learning modelling- Machine learning algorithms and models can be used to further analyse and classify text data based on various criteria, such as topic modelling, clustering, and classification.

There are several tools and libraries available for performing NLP, such as NLTK, SpaCy, and Stanford NLP. These tools provide a range of functionalities and pre-trained models that can be used to perform various NLP tasks.

Applications of NLP

Natural Language Processing has a wide range of applications in various industries. Here are some of the most common applications of NLP:

• Chatbots and Virtual assistants

Chatbots and Virtual assistants are a product of artificial intelligence that is programmed in such a way that they are able to understand natural human language and respond to that appropriately. These artificial intelligence bots are created using natural language processing and machine learning. Nowadays, this technology has become so advanced that it has become difficult to distinguish them from humans. 

• Voice Assistants 

The ones we talked about earlier in this article are virtual voice assistants like Siri, Alexa, and Cortana, and many more are also working on NLP principles. These voice assistants use complex combinations of speech recognition, processing and analysis of natural language and NLP to interpret human voices and respond to them accordingly. It has become a very popular technology in today’s tech-savvy world.

• In Search Engines 

The results that you see appearing themselves when you are searching for something on a search engine is all because of natural language processing. These search engines use massive data sets to analyse what their customers are likely typing when they enter specific words and suggest the most common options. They use NLP to understand how these words are connected to form different sentences.

• Language Translation

Google translate is a widely used tool for translating text from one language to another. This tool is also a product of NLP. It enables the algorithm to translate a sequence of words from one language to another.

• Sentiment Analysis

Sentiment Analyses is a widely used technique used by companies to analyse the sentiments of their users. They can use natural language processing, computational linguistics, text analysis, and other techniques to determine whether users' attitudes towards their products and services are positive, negative, or neutral. This can also be used to monitor social media sentiment and public opinion.

Drawbacks of NLP

Although NLP has numerous benefits, it also has some drawbacks, including—

• Limited Understanding: NLP algorithms often have a limited understanding of the context and meaning of human language. This can lead to errors in interpretation and miscommunication.
• Biased Language: NLP algorithms can be biased based on the language and data used to train them, which can result in discriminatory or unfair outcomes.
• Data Limitations: NLP algorithms require large amounts of data to be trained effectively, which can be costly and time-consuming. Additionally, the quality of the data used to train the algorithms can impact their accuracy and effectiveness.
• Language Variability: Human language is constantly evolving and changing, which can make it difficult for NLP algorithms to keep up and accurately interpret new language patterns and trends.
• Lack of Creativity: NLP algorithms can be good at recognising and reproducing patterns, but they lack creativity and the ability to generate new ideas or responses.

Top Certifications in Natural Language Processing

Following are some top certifications in Natural Language Processing provided by various online platforms that can help you upgrade your skills and land a high-paying job in this field of technology-  

NLP Certification from Coursera

• Offered by Stanford University
• Provides a comprehensive introduction to NLP
• Covers topics such as sentiment analysis, part-of-speech tagging, and language models.

Deep Learning Specialization from Coursera 

• Covers a range of topics related to deep learning, including NLP. 
• Includes five courses and covers topics such as sequence models, attention models, and neural machine translation.

NLP with Python Certification from DataCamp 

• Provides hands-on training in using Python for NLP tasks such as sentiment analysis, text classification, and topic modelling.

Certified NLP Professional from the Association for Computational Linguistics (ACL)

• Covers various aspects of NLP, including syntax and semantics, discourse analysis, and machine learning for NLP.

IBM Watson NLP Certification

• Offered by IBM
• Provides training in using the Watson NLP API for various NLP tasks, such as language translation, sentiment analysis, and entity recognition.

Udacity Natural Language Processing Nanodegree

• Covers various NLP topics, including tokenisation, language modelling, sentiment analysis, and machine translation.
• Includes a capstone project where students can apply their skills to real-world problems.

Top BCA Colleges in India 

Name of the College	Eligibility Criteria	Average Fees	Average Salary Stats
Christ University, Bangalore	10+2 with a minimum of 50% of marks.	₹145,000 (Total Fees)	6.5 LPA
Presidency College, Bangalore	Passed 10+2 from a recognized board with min. 35% in every semester.	₹3.30 Lakh (Total Fees)	3 LPA to 4 LPA
SRM Institute of Science and Technology - [SRMIST], Chennai	A Pass in 10 +2 with Mathematics as a compulsory subject.	₹70000 per year fees	3 LPA to 6 LPA
Symbiosis Institute of Computer Studies and Research - [SICSR], Pune	Passed 10+2 from a recognized board with min. 50% marks.	₹96,500 (first-year fees)	3.55 LPA – 5 LPA
Loyola College, Chennai	Candidates must have passed 10+2 with Mathematics as one of the subjects from the recognized board.	₹144,040/- (Total Average Fees)	3.5 LPA
Vellore Institute of Technology - [VIT University], Vellore	A pass in Class 12 with Mathematics/ Business Mathematics as one of the main subjects and a minimum of 60% aggregate.	₹57,000 (First year Fees)	8.2 LPA
Institute of Business Studies and Research - [IBSAR], Navi Mumbai	A pass in 10+2 or equivalent in any group.	₹95,000 (1st Year Fees)	3 LPA to 5 LPA
Xavier's Institute of Computer Application, Ahmedabad	Candidate must have completed 10+2 with mandatory subjects from a recognized board.	₹31,800 (Total Fees)	7 LPA
DAV College - [DAVC], Chandigarh	Candidate must have passed the 10+2 examination in any discipline with 50% marks from a recognized board.	₹1.11 Lakh (Total Fees)	2.7 LPA

Job Prospects and Career Scope in NLP 

NLP is an exciting and rapidly growing field with a wide range of job prospects and career scope. Here we have listed some of the potential job opportunities and career paths in NLP 

Job Profile	Role and Responsibility	Salary Stats
NLP Engineer	An NLP engineer designs and develops software applications that can understand, interpret, and generate human language. This role typically requires a strong background in computer science, mathematics, and linguistics.	Ranges between ₹3.0 Lakhs to ₹20.0 Lakhs per annum with an average annual salary of ₹7.8 Lakhs.
Data Scientist	A data scientist applies statistical and machine learning techniques to extract insights and knowledge from large datasets, including those involving natural language data. NLP is increasingly being used in various data science applications, such as sentiment analysis, topic modelling, and chatbot development.	Ranges between ₹3.6 Lakhs to ₹25.7 Lakhs per annum with an average annual salary of ₹10.0 Lakhs.
Computational Linguist	A computational linguist develops algorithms and models to analyze and generate human language. This role requires expertise in linguistics, programming, and machine learning.	National Average Salary of ₹40,00,000 per year.
Research Scientist	A research scientist in NLP conducts research to advance the state-of-the-art in natural language processing. This role requires a PhD in computer science, linguistics, or a related field.	Ranges between 4 Lakhs to 28 Lakhs per annum with an annual average salary of  ₹7.2 Lakhs.
NLP Trainer	An NLP trainer designs and delivers training programs to help individuals and organizations develop their NLP skills. This role requires a deep understanding of NLP concepts, as well as excellent communication and teaching skills.	Ranges between 4 Lakhs to 11 Lakhs per annum.
NLP Consultant	An NLP consultant provides expert advice and guidance on NLP-related projects, such as developing chatbots, voice assistants, or others language-based applications. This role requires a broad understanding of NLP and its applications across various industries.	Ranges between 4 Lakhs to 28 Lakhs per annum with an annual average salary of  ₹7.2 Lakhs.
Product Manager	A product manager in NLP is responsible for defining and delivering products that incorporate natural language processing capabilities. This role requires a strong understanding of customer needs, market trends, and technology trends in NLP.	Ranges between ₹5.2 Lakhs to ₹35.0 Lakhs per annum with an average annual salary of ₹17.0 Lakhs

Overall, NLP offers a diverse range of job opportunities and career paths. As the field continues to grow and evolve, there will likely be even more opportunities for those with expertise in natural language processing, and it has the potential to become one of the best-paying jobs in technology in the near future. 

Conclusion 

You may have used Natural Language Processing on a regular basis but had no idea what it was. But now you know about this technology and how it improves our daily lives and contributes to businesses. If you also want to pursue your BCA Degree and get all the practical knowledge about this course, Sunstone is here to help you. 

We, at Sunstone, provide students with a wide range of hands-on capstone projects for skill development every semester. You can get the opportunity to learn from expert faculty (ex-IIMs, IITs) and also get lifetime access to a pan-India student community for further growth. 

FAQ- Natural Language Processing

What programming languages are used in NLP?

Programming languages such as Python, Java, and R are commonly used in NLP.

What skills are needed to learn NLP?

To learn NLP, you should have a strong foundation in programming, machine learning, and statistics. Additionally, familiarity with linguistics and natural language processing concepts can be helpful.