![]() ![]() Theories of argument structure are largely theories about how these additional arguments are introduced, but at present few such theories propose explicit mechanisms for deriving crosslinguistic variation in argument expression. Verbal arguments can be divided into two different types: those that are true arguments of the verb and those that are "additional" in the sense that there is evidence that they do not belong to the basic argument structure of the verb. Languages are often said to lack adjectives entirely, and for some languages it has been claimed that no noun–verb distinction exists, but on closer inspection it is possible to find differences between nouns, verbs, and adjectives in almost all languages. Despite considerable cross-linguistic diversity, there are also clear general trends: e.g., nouns tend to be marked for number, verbs tend to be marked for tense, adjectives tend to occur in a special comparative construction. Nouns, verbs, and adjectives are generally defined by morphosyntactic criteria within a language, but cross-linguistically they can be identified only semantically. There are two main types of word classes: content words (nouns, verbs, adjectives, and adverbs) and function words (adpositions, conjunctions, pronouns, and others). Words can be classified by various criteria, but as a technical term ‘word class’ (or ‘part of speech’) refers to the morphosyntactically defined categories noun, verb, adjective, adverb, adposition, conjunction, pronoun (and a few others). Finally, it is inferred that the adjective type on the most frequent adjective is a describing adjective, which has the function to frame the condition, situation and characteristic of the noun on the COVID-19 cases. Fifth, the data were analyzed based on the related theory. Fourth, 20 the most frequent adjectives were inputted one at a time on concordance. Third, it chose the concordance to comprehend the function of the adjective in the COVID-19 corpus. Second, the data were taken 20 the most frequent adjectives used in COVID-19 corpus because 20 data have already represented the most frequent adjectives. There were several data collection steps those were first, knowing the most frequent adjective in the COVID-19 corpus by choosing a wordlist. The source of the data was corpus about COVID-19 academic writing due to the fact that COVID-19 has been the trending topic around the globe and also became an international concern. The method of this study was a mixed-method by combining quantitative and qualitative approaches. This study was conducted by using a corpus tool named sketchengine. This study aims to investigate the type of adjectives in the most frequent adjectives and also the use of the adjective functions on academic writing about COVID-19. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |