What Is Computational Linguistics? Insights and Career Guide

Written by Coursera • Updated on

Computational linguistics is a field of data science that powers chatbots, search engines, and more. Here are some insights into this fascinating career field.

[Featured Image]: Computational linguist applying algorithms and analyzing a written document.

Have you ever wondered how Alexa can listen and respond to you? Or how a customer service chatbot knows how to respond to your requests? That’s computational linguistics at work.

Computational linguistics (CL) is what powers anything in a machine or device that has to do with language—speaking, writing, reading, and listening. It is often linked with natural language processing (NLP), which is a subset of CL.

Here’s what you need to know about computational linguistics and how to become a computational linguist.

What is computational linguistics (CL)?

Computational linguistics is an interdisciplinary field that applies computer science (algorithms) to analyzing and comprehending 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 language, whether written or spoken, this helps facilitate our interaction with software and machines and enables progress in fields such as customer service, research, AI tools, and much more.

Computational linguistics vs national language processing: what’s the difference?

Although CL and NLP are similar in that they both involve computer science, linguistics, and machine learning, they have slightly different goals.

CL focuses on the system or concept that machines can be computed to understand, learn, or output languages, while NLP 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.

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Why is computational linguistics important?

Computational linguistics as a concept may seem complex. In practice, it requires expert knowledge of machine learning to program software that can not only understand humans but respond meaningfully. Data scientists often analyze large amounts of written text in unstructured formats to build artifacts that can process or produce language.

This is important because today humans are using technology to develop tools for completing tasks more efficiently.  Computational linguistics first emerged to translate languages using computers, such as Chinese to English. These days, it supports customer service, such as making or refunding a purchase online with a chatbot or finding information quickly with the help of Siri on iPhones. 

Examples of CL in the real world

There are many applications of CL in the real world. Here are just a few.

  • 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 can be used to train an open information extractor’s precision and recall.

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

  • Sentiment analysis: This is a type of NLP that 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 gone through several iterations. Here are some key approaches you’ll want to know:

  • Developmental approach: Like a child learning a language over time, the developmental approach simulates a similar language acquisition strategy. Algorithms are programmed 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 underlying structures of the language.

  • Production approach: The production approach uses a CL algorithm to produce text, which can be broken down into text-based or speech-based interactive approaches.

    • Text-based interactive approach: This falls under the production approach, where text written by a human is used to generate an algorithmic response. The computer can then recognize 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 is programmed to naturally interpret written commands using simple rules.

How to become a computational linguist 

Computational linguistics could be in your future career path if you think you might enjoy applying computer science to alter the ways we interact and communicate with computers. Whether as a computational linguist or data scientist focused on natural language processing, you’ll be entrenched in unstructured and structured data, transforming it into something useful.

1. Get a degree.

To get started in computational linguistics, you’ll want to get a 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 that is often required in this career.

According to Zippia, 49 percent of computational linguists have a bachelor’s degree, 39 percent have a master’s degree, and 9 percent hold doctorate degrees, while only 3 percent have an associate degree [1]. Compared to other data science jobs, those numbers are relatively high for post-graduate diplomas.  

From humanities to computational linguist

If you know you want to become a computational linguist early on, it makes sense to build computer science skills before studying linguistics. However, those who study humanities majors, such as 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 pivot from humanities into computational linguistics by learning programming and machine learning later on.

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2. Build up 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 and summarize text, and build chatbots. This specialization from DeepLearning.AI will teach you all this and more.

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specialization

Natural Language Processing

Break into NLP. Master cutting-edge NLP techniques through four hands-on courses! Updated with the latest techniques in October '21.

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Word2vec, Machine Translation, Sentiment Analysis, Transformers, Attention Models, Word Embeddings, Locality-Sensitive Hashing, Vector Space Models, Parts-of-Speech Tagging, N-gram Language Models, Autocorrect, Word Embedding, Sentiment with Neural Nets, Siamese Networks, Natural Language Generation, Named-Entity Recognition, Reformer Models, Neural Machine Translation, Chatterbot, T5+BERT Models

Machine learning

You’ll want to be familiar with concepts like supervised learning, unsupervised learning, and be able to build the right algorithms for CL and NLP. While you can probably learn some of these with YouTube, you can also opt for a comprehensive experience with the machine learning specialization taught by AI visionary and Stanford University researcher Andrew Ng.

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specialization

Machine Learning

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Python (and other programming languages)

In order to program the algorithms used in computational linguistics, you’ll need to learn a programming language. 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 program interfaces. The specialization from the University of Michigan can help you design and create your own applications for retrieving, processing, and visualizing data.

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specialization

Python for Everybody

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Math and statistics

In computational linguistics, it is helpful to develop your skills in math and statistics. You’ll want to master spreadsheet functions, build data models, learn basic probability, and understand how these concepts are used in data science. With the Business Statistics and Analysis Specialization from Rice University, you can apply these skills toward making business decisions relevant to computational linguistics.

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Linguistics

Finally, depending on the type of computational linguistics you are hoping to focus on, it wouldn’t hurt to gain some linguistics knowledge. With a linguistics class, you’ll learn  how your native tongue is similar or different to other languages, key features of both popular and indigenous languages, and more. This free course from Leiden University will feature many accomplished linguists, including Noam Chomsky.

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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 at 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 when entering a smaller field such as computational linguistics. It’s possible to find a fulfilling career here if you excel in computer science and have a knack for linguistics.

Learn computational linguistics

Coursera offers a Natural Language Processing specialization 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 and summarize text, and even build chatbots.

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specialization

Natural Language Processing

Break into NLP. Master cutting-edge NLP techniques through four hands-on courses! Updated with the latest techniques in October '21.

4.6

(4,549 ratings)

87,439 already enrolled

INTERMEDIATE level

Average time: 4 month(s)

Learn at your own pace

Skills you'll build:

Word2vec, Machine Translation, Sentiment Analysis, Transformers, Attention Models, Word Embeddings, Locality-Sensitive Hashing, Vector Space Models, Parts-of-Speech Tagging, N-gram Language Models, Autocorrect, Word Embedding, Sentiment with Neural Nets, Siamese Networks, Natural Language Generation, Named-Entity Recognition, Reformer Models, Neural Machine Translation, Chatterbot, T5+BERT Models

Article sources

  1. Zippia. “Computational Linguist Education Requirements, https://www.zippia.com/computational-linguist-jobs/education/.” Accessed September 28, 2022.

Written by Coursera • Updated on

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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