How linguistics can be applied to real-world problems.
Defining applied linguistics
Applied linguistics is an interdisciplinary field that combines knowledge and methods from linguistics, psychology, sociology, education, and other fields to understand and solve real-world problems related to language.
The field can be broken down into several categories.
Second language acquisition studies how people learn a second or foreign language and how to make the learning process more effective.
Language assessment focuses on developing methods for assessing language proficiency and language ability, such as tests and evaluations for academic or professional purposes.
Discourse analysis studies the use of language in different contexts, such as in conversation, in the media, or in the workplace, to understand how people use language to communicate and interact.
Forensic linguists apply linguistic theories, methods, and findings to legal and forensic issues.
Finally, language planning and policy focuses on the social and political factors that affect language use and develops policies and plans to promote language maintenance and revitalization.
Exploring forensic linguistics: The analysis of language for legal purposes
Forensic linguistics is a subfield of applied linguistics that applies linguistic knowledge and methods to legal issues and the administration of justice. Forensic linguists use their expertise in language and communication to assist with tasks such as authorship attribution, speaker identification, and the interpretation of legal texts.
Forensic linguists often analyze written and spoken language in order to identify the author or speaker. This can include analyzing linguistic features such as grammar, vocabulary, and writing style, as well as non-linguistic factors such as handwriting and voice patterns.
Forensic linguists may use computational methods, such as text-comparison software, to analyze large amounts of language data. They also analyze legal texts, such as contracts, statutes, and court transcripts. This involves interpreting the meaning and intent of the language used in these texts, and identifying any ambiguities or inconsistencies that may be relevant to a legal case.
Language teaching and learning: Pedagogical approaches
Pedagogical approaches are the ways in which language is taught in formal educational settings. One of the most well-known and widely-used approaches is the communicative approach, which emphasizes the use of authentic language in real-life situations.
This approach aims to develop students’ communicative competence, or the ability to use language effectively in a variety of contexts.
To achieve this, teachers may use techniques such as task-based language teaching, where students are given tasks to complete using the language they are learning, and problem-based learning, where students work together to solve real-life problems using the language.
The cognitive approach emphasizes the role of the learner’s cognitive processes in the language learning process. This approach is based on the idea that language learning is an active process, where learners construct their own understanding of the language.
Discourse analysis: The study of language in context
Discourse analysis is a method of studying language in use, in order to understand the ways in which language functions in communication and how it reflects and shapes social relations, identities and power.
There are several key concepts in discourse analysis that are central to understanding how language is used in communication. “Discourse” refers to a stretch of language that is organized and meaningful, such as a conversation, a written text, or a speech. “Discursive practices” are the ways in which people use language to accomplish social actions, such as persuading, informing, or entertaining.
“Discursive events” refer to specific instances of discourse, such as a trial, a news broadcast, or a political debate. “Discursive formations” refer to the ways in which discourse is organized across different contexts, such as across different media or different historical periods.
Discourse analysts pay attention to the ways in which power is embedded in discourse, how discourse is used to reproduce and challenge relations of power, and how it helps to establish, maintain or challenge social hierarchies.
How applied linguistics is used to study language disorders
Applied linguists use their expertise in language and communication to understand the nature and causes of language disorders, as well as to develop effective methods for diagnosis and treatment.
They may analyze spoken or written language samples from individuals with language disorders in order to identify specific language impairments and to understand how these impairments affect the individual’s ability to use and understand language.
Applied linguists may work with speech-language pathologists and other specialists to develop standardized tests and procedures for the diagnosis of language disorders, such as language assessment batteries, which are used to assess various aspects of language abilities.
They may conduct research on the effectiveness of different therapies and interventions, such as speech therapy, cognitive therapy, or computer-based interventions, and provide recommendations on the most effective approaches.
Technology and applied linguistics: Computational tools in language research
Computational tools can be used to analyze language data and understand language use in real-world contexts. Corpus tools are software programs that are used to create, annotate, and analyze large text collections, known as corpora.
These tools allow researchers to quickly search for specific patterns or words in a corpus, and to create frequency lists and concordances. Part-of-speech (POS) taggers automatically label words in a text with their grammatical function, such as noun, verb, adjective, etc. POS taggers are useful for tasks such as syntactic parsing and grammatical error detection.
Parsers analyze the grammatical structure of a sentence. Machine learning tools can be used to train models for tasks such as text classification, sentiment analysis, and machine translation.
Machine learning is increasingly used in applied linguistics to analyze large, unstructured language data, such as social media posts, and to perform tasks such as sentiment analysis and opinion mining.
