Computational Linguistics, is an interdisciplinary field that combines principles of linguistics and computer science to develop algorithms, software, and systems for the automatic processing and understanding of human language. It focuses on enabling computers to work with and understand human language in a way that is both meaningful and useful.

Professionals in the field of Computational Linguistics are typically referred to as:

Computational Linguists: These individuals apply linguistic theories and methods to develop computational models and solutions for language-related tasks.

Natural Language Processing (NLP) Engineers: NLP engineers often work on the practical implementation of NLP technologies, including the development of algorithms and software for tasks such as text analysis, machine translation, speech recognition, and more.

Linguistic Data Scientists: Linguistic data scientists focus on collecting, processing, and analyzing linguistic data to improve language-related systems.

Machine Learning Engineers: Many professionals in this field work on the machine learning and artificial intelligence aspects of NLP, such as training models to understand and generate human language.

1. Making Computers Understand Language: They create special computer programs that help computers understand and work with human languages, like English or Spanish.
2. Translating Languages: They work on tools that make it possible for computers to translate languages, like changing English into French.
3. Recognizing Speech: They develop systems that can listen to spoken words and turn them into written text, which is used in voice assistants and transcriptions.
4. Finding Information: They build search engines that can understand what you're looking for when you type a question and give you the right answers.
5. Analyzing Emotions: They create tools that can figure out if a piece of text (like a tweet) is happy, sad, or neutral, which is helpful for things like social media monitoring.
6. Creating Text: They make computer programs that can write text that looks like it was written by a human, which is used in chatbots and content generation.
7. Collecting Language Data: They gather and organize big sets of language data that computers can learn from, like dictionaries and example sentences.
8. Checking Quality: They come up with ways to test how well these language programs are doing and make sure they work correctly.
9. Training Computer Models: They teach computer models to understand and generate language, kind of like training a pet to do tricks.
10. Teaching and Research: Some of them work at schools and universities, where they teach others about language and do experiments to discover new things about how computers can understand language better.

Step 1:  Class 10-12 (High School): Complete your high school education with a focus on subjects like mathematics, computer science, and linguistics if available.

Step 2:  Undergraduate Studies (Bachelor's Degree): After completing high school, you should pursue a bachelor's degree. While there isn't a specific bachelor's degree in computational linguistics, you can choose a related field such as:

Computer Science: A Bachelor of Computer Science program will provide a strong foundation in computer programming and algorithms, which are essential for computational linguistics.

Linguistics: A Bachelor of Linguistics degree can provide a solid background in language and linguistic theory, which is a crucial component of computational linguistics.

Data Science: Some universities offer data science programs that include courses in natural language processing, which can be a good starting point.

Step 3:  Graduate Studies (Master's or Ph.D.): To specialize in computational linguistics, consider pursuing a master's or Ph.D. in the field. Some universities in India and abroad offer dedicated programs in NLP or computational linguistics. A master's degree can take 2-3 years, while a Ph.D. may take several more years.

Becoming a computational linguist is a journey that involves a strong foundation in both linguistics and computer science, as well as practical experience and ongoing learning. Your path may vary depending on your interests and the opportunities available, but a strong educational background and programming skills are essential.

• NLP Engineer: Develops language-processing algorithms.
• Machine Learning Engineer (NLP): Trains ML models for language tasks.
• Linguistic Data Scientist: Analyzes linguistic data for NLP.
• Research Scientist (NLP): Conducts language-related research.
• Speech Recognition Engineer: Converts speech to text.
• Information Retrieval Specialist: Creates search engines.
• Machine Translation Specialist: Improves translation systems.
• Text Generation Specialist: Generates human-like text.
• Language Resource Curator: Manages linguistic resources.
• Chatbot Developer: Builds conversational agents.
• AI Ethics Researcher: Ensures ethical AI use.
• Educator: Teaches NLP and conducts research.
• Content Manager: Optimizes content using NLP.
• Government and Defense (NLP): Works on language-related security.
• AI Product Manager: Oversees AI product development.

• Research Institutions
• Language Technology Companies
• Academia
• Healthcare
• Financial Services
• Government and Defense
• Social Media and Marketing
• Content Generation and Publishing
• Customer Service and Chatbots
• Information Retrieval and Search
• Legal Services
• Education
• Translation and Localization
• Ethical AI and Fairness Advocacy

The salary and perks for computational linguists can vary significantly depending on factors like experience, location, education, and the specific industry or sector they work in. Here are some general insights into the compensation and perks for professionals in this field:

Salary: The salary of computational linguists can range widely. In India, for example, entry-level positions may offer annual salaries between INR 4-8 lakhs, while experienced professionals can earn significantly more, sometimes exceeding INR 20 lakhs or more per year. 

Perks and Benefits: Along with your salary, you might get things like health insurance, retirement plans, bonuses, stock in companies, work flexibility, time off, and opportunities for learning and travel. The exact perks depend on your job and the company you work for.