The intersection of computer science and linguistics

What is computational linguistics?

TechTarget define computational linguistics (CL) as the ‘application of computer science to the analysis and comprehension of written and spoken language.’ CL is an interdisciplinary field that focuses on combining computer science – in particular, artificial intelligence (AI) and machine learning (ML) – with linguistics, engineering, and cognitive science/neuroscience to understand various aspects of language from a computational perspective.

It achieves this by developing rule-based models and algorithms that enable computer systems to process and analyse human language. Both computer science and linguistics focus on semantics, syntax, synthesis, and instructions – and, importantly, how humans interact with, and perceive, them.

Why is computational linguistics useful?

Computational linguistics is foundational in advancing both the technology and tools that help us to complete tasks more efficiently. Originally developed for language translation purposes – for example, Chinese to English – it’s evolved and transformed entire industries, such as customer service.

Machines that are linguistically competent help bridge the gap between humans and computer software and facilitate interaction. This is particularly important in helping us to navigate our increasingly interconnected, technologically driven, globalised world. It enhances communication, boosts productivity, drives natural language understanding and diversity, contributes to language learning and education, supports accessibility, and advances research and knowledge in areas from sociolinguistics and linguistics theory to human behaviour.

What are some real-world applications of computational linguistics?

Ever wondered what technology powers your French language learning on DuoLingo? How search engines work? Why Netflix seems to understand the films and tv shows you’d most like?

CL plays a critical role in advancing our understanding of language, and enabling us to develop intelligent systems that have the ability to interact with humans in natural language. Whether we’re aware of it or not, its widespread use in our everyday lives powers all sorts of technology, from text-to-speech synthesisers, spell checks and search engines to parsers, translation technologies and interactive speech recognition systems.

There are a wide variety of computational linguistics use cases:

• Machine translation – where AI is used to translate between human languages. For example, Google Translate.
• Text mining – where useful information is extracted from large quantities of unstructured textual data. For example, using analytics language technology to mine online reviews, customer surveys, and call centre transcripts.
• Information retrieval and information extraction – where knowledge is created from structured and unstructured textual data. For example, searching webpages, documents, and other types of content for specific terms.
• Content filtering – where ML techniques block forms of language-based web content from reaching users. For example, email filters that identify certain emails as “junk”.
• Natural language interfaces – where words or phrases act as controls in computer-human interfaces. For example, virtual assistants such as Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortana, or Google Assistant.
• Sentiment analysis – where emotional tone is verified from a body of text. For example, social media monitoring.
• Chatbots – where human conversation (or ‘chat) is simulated by software through speech or text interactions. For example, pop-up chat windows on websites and apps that offer to help redirect users or answer questions.

For more information, resources such as MIT Press Direct’s Computational Linguistics journal – which publishes cutting-edge articles across all areas of the discipline – or content from Christopher Manning (Director of the Stanford AI lab), which covers topics such as parsing, are great jump-off points.

How is computational linguistics different from natural language processing?

Natural language processing (NLP) is a branch of AI. According to IBM, NLP ‘combines computational linguistics with statistical and machine learning models to enable computers and digital devices to recognise, understand, and generate text and speech.’

It’s a term often used interchangeably with computational linguistics and, while the two are closely linked, they are not one and the same. Rather, while CL is the broader scientific and theoretical framework for understanding human language, NLP focuses specifically on the computational models, technologies, methods, and solutions for processing natural language data.

Career paths in computer science and linguistics

Computer scientists rely on corpus linguistics to advance programming, and linguists rely on computer science to understand and analyse language in greater depth. By choosing a computer science degree that teaches critical programming language models, AI, ML, deep learning (DL) and data science skills, you’ll boost your competitive advantage and set yourself to excel in your future career.

Individuals with knowledge of both computer science and linguistics are well placed to carve out any number of interdisciplinary careers. Your specialist expertise and insights will serve you well in a wide range of roles, including:

•         data scientist or data analyst
•         machine learning engineer
•         software developer
•         natural language processing (NLP) scientist
•         artificial intelligence engineer
•         business intelligence officer
•         computational linguist
•         language engineer
•         computer programmer
•         IT project manager.

Roles that require skills associated with computational linguistics are in high demand. Data from the Bureau of Labor Statistics (BLS) forecasts 23% growth in employment for computer and information research scientists between 2020 and 2030 (much faster than the average for all occupations) – with those able to develop new programming languages and applications in particular demand. BLS put the median annual salary (correct as of May 2020) in this field at $126,830 (£99,682).

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