Applied Linguistics

• Fields of Study: Computer Science; Programming Language; Software Engineering

Abstract

Applied linguistics is a linguistics subfield that focuses on solving the linguistics problems of daily life. Areas of interest include language learning, language preservation, and automated linguistic tools. Applied and computational linguistics overlap in the design of algorithms and computer programs for educational and commercial applications.

Linguistics and Technology

Applied linguistics seeks to address real-world language issues. While its emergence predates computer science, modern applied linguistics overlaps with computational linguistics. In particular, computer science tools are useful for modeling linguistic concepts and creating automated translation tools. Applied and computational linguistics research has produced many internet-based language learning programs, as well as programs that allow machines to recognize and respond to linguistic cues.

Development of the Field

Applied linguistics was first recognized as a distinct field of linguistics research in the 1950s. However, the application of linguistics knowledge is a far older tradition within academic linguistics. Some of the first applied linguistics departments and organizations in the United States focused on guiding language policy in government initiatives.

Another early area of concern was the application of linguistics research to language learning, both for native language speakers and for those learning a second language. In the 1960s, linguistics researchers began working on ways to use computer technology in linguistics education and research. Computer-assisted language learning (CALL), first developed in the 1960s, studies ways to use computers to assist in language acquisition. In the twenty-first century, tools and techniques developed for CALL programs are regularly used to design software-based language learning programs.

Computational linguistics focuses on both applied and theoretical research. It grew out of military efforts in the 1950s to use computers to translate foreign languages, particularly Russian. Computers were widely used in linguistics research by the late 1980s. In the 1990s and 2000s, web-based linguistic applications began to become common.

Linguistics research focuses generally on "natural languages," which develop "naturally" through human use and refinement. They are distinct from formal languages, such as computer programming languages, and constructed languages, such as Esperanto. Developing better learning materials, both traditional text-based materials and modern software-based materials, is a primary focus of applied linguistics.

Linguistic Analysis

One major area of linguistics research is linguistic analysis, which seeks to dissect natural languages into their component parts to better understand how languages function and change. Each language can be analyzed in terms of its lexicon. This is the total vocabulary available in a certain language or subset of a language. The term can also refer to an individual's total vocabulary. To understand a language, its specific lexicon must be broken into smaller units. Linguists may study the morphology of words (the specific pattern of characters and phonemes that make up each word). They may also study a language's semantics (how words and phrases relate to what they represent). Beyond semantics is syntax, which looks at how words and phrases are arranged to form sentences and other higher-order units of language. In the digital age, linguistic analysis increasingly relies on software that can compile and evaluate properties of language.

Computer Science Applications

Automated translation is among the best-known examples of applied computational linguistics. Web-based services such as Babelfish, Google Translate, and Apple's Translate app use algorithms to analyze user-provided words and sentences and translate the semantic concepts into equivalent expressions in other languages. Computer scientists and linguists are working to refine such software to provide more accurate translations. Applied linguistics has also resulted in the development of software systems that allow users to "speak" to automated systems. Apple's iOS assistant Siri is an example of a program that can analyze the syntax and semantics of a spoken prompt and respond in kind.

Computer science and linguistics have created innovative solutions to a wide range of linguistic problems. For instance, the Enable Talk glove is a Bluetooth-enabled glove that, when worn, can translate sign language into spoken language. This enables sign-language users to communicate with individuals who do not understand sign language. Various similar therapeutic applications exist for individuals with speech, hearing, or other linguistic challenges. Another real-world issue being addressed through applied linguistics is the preservation, documentation, and analysis of the world's "endangered languages." These languages are in danger of being lost because of a lack of native speakers or a low rate of transmission to new generations.

Applied computational linguistic developers have also created tools that can search through text and written data. Computer scientists and linguistics specialists have developed software that can use programmed linguistic rules and structural cues to locate keywords, sentences, and other elements within text. Basic keyword searches are one type of computational linguistic search tool. Computer scientists have also developed search tools that allow more complex searches and meta-searches within text data through artificial intelligence (AI). However, these tools still have shortcomings as they are based on structured queries to get the most relevant and accurate results.

Overall, though, the rise of AI and machine learning has reshaped the study of applied linguistics. OpenAI, a research organization and technology company, has been a leader in AI-generated communication. Their mission is focused on developing and promoting artificial intelligence in a safe and beneficial way. For example, in June 2018, OpenAI introduced the groundbreaking GPT-1 model, a milestone in natural language processing with 117 million parameters, marking a key advancement in machine learning. This was followed by GPT-2 in 2019, which offered improved text generation, and the even more powerful GPT-3 in 2020 with 175 billion parameters, paving the way for GPT-3.5 in 2022, which became the foundation for ChatGPT, a means of advanced conversational AI that offers real-time feedback and personalized learning experiences.

AI has transformed applied linguistics by providing innovative tools for language learning, teaching, and research. Models like ChatGPT offer real-time feedback and personalized learning experiences, while applied linguistics informs AI development by analyzing how humans process language. However, concerns about bias and overreliance on AI underscore the need for ethical and mindful integration in education.

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