What Is Computational Linguistics? Insights and Career Guide

When dealing with how computers intersect with linguistics (the study of language), the field of computational linguistics (CL) focuses on where natural language fits into this intersection. For example, you may wonder how Alexa can listen and respond to you. Or, perhaps you have thought about how a customer service chatbot knows how to respond to your requests. That is computational linguistics at work.

Computational linguistics is what powers anything in a machine or device that has to do with language—speaking, writing, reading, and listening. It often links with natural language processing (NLP), a subset of CL. Also, if you are considering it as a career path, you might like to know that a computational linguist typically earns a good paycheck. According to Salary Expert, the average annual salary for this position is £40,391 [1].

Discover what you need to know about computational linguistics and how to enter the field. 

What is computational linguistics?

Computational linguistics is an interdisciplinary field that applies computer science (and the use of algorithms) to analyse and comprehend written and spoken language. The field combines linguistics, computer science, artificial intelligence (AI), engineering, neuroscience, and even anthropology to understand language from a computational perspective. 

When a computer can understand written or spoken language, this helps facilitate your interaction with software and machines and enables progress in fields such as customer service, scientific research, AI tools, and much more.

Computational linguistics vs. national language processing

Computational linguistics focuses on the system or concept that humans can programme machines to understand, learn, or output languages. In contrast, natural language processing (NLP) is the application of processing language that enables a computer programme to understand written or spoken human language.

Put simply, computational linguistics encompasses more than just NLP because it also covers text mining, information extraction, machine translation, and more.

Why is computational linguistics important?

CL is vital today because humans use technology to develop tools for completing tasks more efficiently. Computational linguistics first emerged to translate languages, such as Chinese to English, using computers. Now, it supports customer service, such as when you try to refund a product with a chatbot or find information quickly with the help of Siri on iPhones. Computational linguistics is the process of deciphering what customers are asking and prompting AI to deliver accurate responses to their questions based on internal data.

Data scientists often analyse large amounts of written text in unstructured formats to build artefacts that can process or produce language. They ensure a chatbot or app is giving high-quality service, so that engineers can use computational models to define the system's guidelines.

Examples of computational linguistics in the real world

You’ll find many applications of CL in the real world, a few being machine translation, chatbots, and sentiment analysis. Examples include: 

• Machine translation: Using AI to translate from one language to another, such as from Chinese to English. Google Translate is a good example.

• Chatbots: Software programs that simulate human conversation via spoken or written language, usually for customer service purposes. Many companies, such as Amazon and Verizon, have live chat available alongside phone and email options.

• Knowledge extraction: Creating knowledge from unstructured and structured text sources. An example is Wikipedia, which is the product of random editors, and you can use it to train an open information extractor’s precision and recall.

• Natural language interface: These types of tools allow humans to interact with your devices’ operating systems using spoken words. Examples include Siri and Alexa.

• Sentiment analysis: This type of NLP identifies emotional tone in text or spoken language. Grammarly is an example of sentiment analysis.

Approaches to computational linguistics

Since its inception in the 1950s, computational linguistics has undergone several iterations, including the developmental, structural, and production approaches. Take a look in more detail at some essential approaches people use today:

• Developmental approach: Like a child learning a language over time, the developmental approach simulates a similar language acquisition strategy. Professionals programme algorithms to adopt a statistical approach that does not involve grammar.

• Structural approach: This approach is more theoretical and runs large samples of a language through CL models to better understand its underlying structures.

• Production approach: The production approach uses a CL algorithm to produce text and break it into text-based or speech-based interactive approaches.

• Text-based interactive approach: This falls under the production approach, where humans write text, which they use to generate an algorithmic response. The computer can then recognise patterns and produce a response based on user input and keywords.

• Speech-based interactive approach: Similar to the text-based approach, this one uses algorithms to screen speech inputs for sound waves and patterns.

• Comprehension approach: With this approach, the NLP engine naturally interprets written commands using simple rules.

How to become a computational linguist 

To become a computational linguist, you need to consider earning a master’s degree in computational linguistics after earning your bachelor’s degree in a related subject, something in the sciences or humanities. Additionally, if you think you might enjoy applying computer science to alter how people interact and communicate with computers, computational linguistics could be your future career path. Whilst working as a computational linguist, you are typically entrenched in unstructured and structured data, transforming it into something useful.

1. Get a master’s degree

To begin working as a computational linguist, you’ll want to get a master’s degree in computational linguistics. Not only will this build a strong foundation of understanding computers, but you’ll also gain a credential. Furthermore, the vast majority of computational linguists have earned a master’s degree because, according to Salary Expert, 70 per cent of people working in this field have one [1].

From humanities to computational linguist

If you know you want to become a computational linguist early on, building computer science skills before studying linguistics makes sense. However, those who study linguistics, history, or literature may find themselves passionate about preserving indigenous languages or wanting to build an app for translating between languages (like Google Translate or VoiceTra). 

2. Build up your skills

Because this is a technical field, building your skill set in areas like NLP, machine learning, and programming languages will likely make you more effective at your job. Review these components of computational linguistics in more detail, and explore why you need to develop your understanding of them further.

Natural language processing

You’ll want to learn the specific algorithms and models for natural language processing applications, like question-answering and sentiment analysis, as well as tools that translate languages, summarise text, and build chatbots. The Deep Learning Specialisation from DeepLearning.AI can help you understand all this and more.

Machine learning

You’ll want to be familiar with concepts like supervised and unsupervised learning and be able to build suitable algorithms. Opt for a comprehensive introduction with the Machine Learning Specialisation taught by AI visionary Andrew Ng.

Python (and other programming languages)

You'll need to learn a programming language to programme the algorithms used in computational linguistics. Python is a good one to start with because it is one of the most commonly used. You’ll want to learn data structures, databases, and application programme interfaces. The Specialisation from the University of Michigan can help you design and create your own applications for retrieving, processing, and visualising data.

Maths and statistics

In computational linguistics, developing your maths and statistics skills is helpful. You’ll want to master spreadsheet functions, build data models, learn basic probability, and understand how these concepts are used in data science. The Business Statistics and Analysis Specialisation from Rice University can help you apply these skills.

Linguistics

Finally, it wouldn’t hurt to gain some actual linguistics knowledge. This course from Leiden University features many accomplished linguists, including Noam Chomsky.

3. Apply for jobs

Once you feel comfortable with your skills and knowledge of computational linguistics, you may be ready to apply for jobs and begin networking.

A few popular employers for computational linguists in the UK are the NHS, Google, Relative Insight, BeyondWords, and Code Disruptors. Because this field is relatively niche, you may find that roles in computational linguistics are typically available in tech companies. However, as you can see in the previous statement, other institutions, such as the NHS, are searching for people with this skill set. Do your research when entering a smaller field, such as computational linguistics. Finding a fulfilling career here is possible if you excel in computer science and have a knack for linguistics.

Learn computational linguistics

Computational linguistics is a technical discipline requiring a solid grasp of language principles. DeepLearning.AI offers a Natural Language Processing Specialisation on Coursera, and NLP is a key component of artificial intelligence and uses computational linguistics to accomplish its goals. You can learn how to design applications that perform question-answering and sentiment analysis, create tools to translate languages, summarise text, and even build chatbots.