What Is Computational Linguistics? Insights and Career Guide

You may wonder how Alexa can listen to you or how a customer service chatbot knows how to respond to your requests. That’s computational linguistics (CL) at work.

Computational linguistics powers anything related to language in a machine or device, including speaking, writing, reading, and listening. It often links with natural language processing (NLP), a subset of CL.

Explore what you need to know about computational linguistics and how to become a linguist.

What is computational linguistics (CL)?

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 our 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 machines can be programmed to understand, learn, or output languages, whilst natural language processing is the application of processing language that enables a computer program to understand human language as it is written or spoken.

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 important because today, humans use technology to develop tools to complete 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 deciphers what customers are asking and prompts AI to respond accurately based on internal data.

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

Examples of computational linguistics in the real world

From the Chatbots you interact with to the language translation apps you use, you may already be using computational linguistics without realising it. You’ll find many applications of CL in the real world, including: 

• Machine translation: Using AI to translate from one language to another, such as 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. Many companies, such as Amazon and Verizon, have live chat options alongside phone and email.

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

• Natural language interface: These tools allow humans to use spoken words to interact with our devices’ operating systems. 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. Outlined below are some fundamental approaches people use today:

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

• Structural approach: This approach is more theoretical and involves running large language samples through CL models to better understand their underlying structures.

• Production approach: The production approach uses a CL algorithm to produce text, which further divides into text-based or speech-based interactive approaches.

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

• Speech-based interactive approach: Similar to the text-based approach, this 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 

Computational linguistics could be your future career if you enjoy applying computer science to alter how people interact and communicate with computers. Computational linguists are entrenched in unstructured and structured data, transforming it into something useful.

1. Get a master’s degree.

To start your career in computational linguistics, you’ll want to get a master’s degree in computer science, linguistics, or a related field. Not only will this build a strong foundation of understanding computers, but you’ll also gain a credential many employers require for this career.

From humanities to computational linguists

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). It’s entirely possible to get into computational linguistics by learning coding and machine learning.

2. Build your skills.

To become a computational linguist, you’ll need to build the following skills:

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. This Specialisation from DeepLearning.AI will teach you 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 to the Machine Learning Specialisation taught by AI visionary Andrew Ng.

Python (and other programming languages)

You'll need to learn a programming language to program the algorithms used in computational linguistics. Python is a good one because of its popularity and ease of learning. Explore data structures, databases, and application program interfaces. The Specialisation from the University of Michigan can help you design and create your 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 relate to 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.

Because this field is relatively niche, you may find that roles in computational linguistics are only available at big tech companies like Amazon, where machine learning data linguists and language engineers work on Alexa, or Apple, where computational linguists and speech engineers develop Siri. Other companies, like Grammarly, may hire linguists with a data science background to work out the kinks in their software.

Do your research before entering a smaller field, such as computational linguistics. If you excel in computer science and have a knack for linguistics, you can find a fulfilling career here.

Learn computational linguistics on Coursera

Computational linguistics is a technical discipline requiring a solid grasp of language principles. Coursera offers a Natural Language Processing Specialisation from DeepLearning.AI, one of the most broadly applied areas of machine learning. You’ll learn how to design applications that perform question-answering and sentiment analysis, create tools to translate languages, summarise text, and even build chatbots.