Corpus linguistics is one of the fastest-growing methodologies in contemporary linguistics. In a conversational format, this article answers a few questions that corpus linguists regularly face from linguists who have not used corpus-based methods so far. It discusses some of the central assumptions (‘formal distributional differences reflect functional differences’), notions (corpora, representativity and balancedness, markup and annotation), and methods of corpus linguistics (frequency lists, concordances, collocations), and discusses a few ways in which the discipline still needs to mature. At a recent LSA meeting … [with an obvious bow to Frederick Newmeyer] Question: So, I hear you’re a corpus linguist. Interesting, I get to see more and more

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1749-818X.2009.00149.x

What is Corpus Linguistics

Semantics is composed meaning of language expressions and perceptions at the levels of words, phrase, sentence, paragraph, and essay. The basic unit of formal semantics is a concept as a formal model of words in a natural language. Cognitive linguistics focuses on cognitive semantics and its interaction with underlying syntactic structures. A denotational mathematics known as semantic algebra is presented for rigorous semantics manipulations in cognitive linguistics. Semantic algebra deals with formal semantics of linguistics by a general mathematical model of semantics and a set of algebraic operators. On the basis of semantic algebra, language semantics may not only be deductively analyzed based on syntactic structures from the top down, but also be synthetically composed by the algebraic semantic operators from the bottom up at different levels of language units. Semantic algebra enables a wide range of applications in cognitive informatics, cognitive linguistics, computational linguistics, semantic computing, cognitive computing, machine learning, computing with words, as well as natural language analysis, synthesis, and comprehension.

https://www.researchgate.net/profile/Yingxu_Wang/publication/263891528_On_Semantic_Algebra_A_Denotational_Mathematics_for_Cognitive_Linguistics_Machine_Learning_and_Cognitive_Computing/links/5441da390cf2e6f0c0f66110.pdf

On Semantic Algebra: A Denotational Mathematics for Natural Language Comprehension and Cognitive Computing

Recent work has shown that many problematic aspects of metaphor use and comprehension can be resolved through an account that includes both relevance and perceptual simulation. It has also been shown that metaphors often imply stories, and that stories are often metaphorical. Previous research on narratives has focused primarily on stories that appear either in formal literature or in structured interviews; this essay focuses on stories that occur as an integral part of conversation. It extends recent work on metaphor comprehension to show how use and comprehension of stories in natural, informal conversations can be usefully analyzed in terms of perceptual simulations. Conversely, it extends previous work on storytelling to reveal the metaphorical element in many stories, and to emphasize the social (bonding through shared enjoyment) as well as informative and persuasive functions of storytelling.

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“Everybody goes down”: Metaphors, Stories, and Simulations in Conversations

When studying verbal materials, both adults and children are often poor at accurately monitoring their level of learning and regulating their subsequent restudy of materials, which leads to suboptimal test performance. The present experiment investigated how monitoring accuracy and regulation of study could be improved when learning idiomatic phrases. Elementary school children (fourth and sixth-graders) were instructed to predict their test performance by providing judgments of learning (JOLs). They provided JOLs immediately after studying each idiom, after studying all idioms, or after studying all idioms followed by generating sentences in which the idioms were used. Correlations between JOLs and test performance showed that delayed-JOLs and delayed-JOLs with sentence generation were more accurate than immediate JOLs. JOLs after sentence generation also improved regulation of study compared to delayed-JOLs only. Analyses of JOL reaction times suggest that delayed-JOLs led children to retrieve the literal meaning of the idiom, whereas JOLs after sentence generation led children to focus on connections between studied information, contextual information, and prior knowledge. Sentence generation presents a promising method to improve regulation of study, and thus idiom learning.

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The Effect of Delayed-JOLs and Sentence Generation on Children's Monitoring Accuracy and Regulation of Idiom Study.

It is recognized that formal grammars and rigorous syntactic rules are indispensable in formal, cognitive, and computational linguistics. The formal syntax of linguistics can be classified into two categories known as the analytic and synthetic syntax. The former are a set of lexical rules of individual words, phrases, and parts of speech; while the latter are a set of relational rules of formal syntaxes within and beyond sentences. The synthetic syntaxes can be formally embodied by a set of relational syntactic rules for lexis, phrases, and parts of speech within and beyond sentences. A theoretical framework of formal linguistics is coherently presented by analytic and synthetic syntaxes based on contemporary denotational mathematics. The formalization of natural language syntaxes and grammars enables a wide range of applications not only in cognitive informatics, cognitive linguistics, natural language processing, cognitive computing, semantic computing, cognitive robotics, and computational linguistics in general, but also in word processing, web search engines, online text processing, machine-enabled language comprehension, autonomous machine learning, cognitive translators, computing with words, and cognitive systems in particular.

Formal Relational Rules of English Syntax for Cognitive Linguistics, Machine Learning, and Cognitive Computing

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Andreas Langlotz. 2006. Idiomatic Creativity: A Cognitive Linguistic Model of Idiom-Representations and Idiom-Variation in English. Amsterdam - Philadelphia: John Benjamins.

Sanja Vakanjac

Papers

Andreas Langlotz. 2006. Idiomatic Creativity: A Cognitive Linguistic Model of Idiom-Representations and Idiom-Variation in English. Amsterdam - Philadelphia: John Benjamins.