مطالب

In the present study, we analyze the semantic fragmentation and conventionalization in Persian compound nouns ending in the verbal stems –andāz ‘throw’, -band ‘fasten/close,’ -foruš ‘sell’, -gir ‘catch’, -keš ‘pull’, -paz ‘cook’, -yāb ‘find’, and -zan ‘hit’ from the usage-based perspective. The analysis is based on a 800 data set extracted from diachronic and synchronic corpora. The words produced from the general compounding pattern [XV PRS]N can be categorized in a range of semantic categories, including agent, instrument, location, and object. In describing the semantic fragmentation of [XV PRS]N, we propose the human agent as the starting meaning, from which the instrument sub-pattern is derived by the mechanism of metaphorical extension. However, to justify the object and location meanings, we consider the metonymic extension mechanism to be involved. The sense extension mechanisms do not only apply to the individual words but can happen on the pattern level. It is also argued that these mechanisms are not mechanically applied to all the patterns ending in the verbal stems, instead, it is the usage and the communicative needs of the speakers that determine the semantic fragmentation of any patterns. To illustrate this point, as a case study, we focus on the development of instrument meaning in the pattern [X-paz PRS]N. We show that the instrument sub-pattern is a recent linguistic phenomenon that coincides with the introduction of modern cooking equipment with mostly English names to Iranian society. The increasing use of these types of equipment has led to a new communicative need for naming such instruments. This extra-linguistic factor has motivated the pattern [X-paz PRS]N to be extended through analogy with English compound instrument nouns. The findings of this study may contribute to the understanding of word-formation patterns in general and compounding patterns in particular.

Since 2010, deep learning has been further developed, and the concept of multi-modality has penetrated into all walks of life. However, it has not been fully researched and applied in college English teaching, so this study modeled and practiced the multimodal teaching method of college English under the deep learning mode and its application. The definitions of modality and medium are first introduced, and then the definition of multimodality in this study is clarified. Then the classification of multimodal transport is expounded. The random forest algorithm is chosen as the main algorithm of this research, and a dynamic multimodal model is established. After that, there was a collaboration with a university and sophomore students were selected for practice. After processing and analyzing the collected data, it was found that in the data sample of 268 students, the number of students who did not study independently accounted for 24%, which indicates that most college students lack interest in learning English. Preliminary tests were also conducted on students' English proficiency throughout the year, and the results showed that the students' English proficiency was at a pass level and the overall English proficiency was weak. Reassessment of students' English proficiency showed that the actual teaching effect of each English proficiency was greater than 85%, and the effectiveness of English teaching in the selected universities was significantly improved. The average score improved by 8 points, indicating that multimodal teaching is scientifically effective After a semester of multimodal teaching, the English teaching effectiveness of the university selected in this article has significantly improved. The research results indicate that the development of deep computer learning has introduced multimodal concepts into the teaching field, which is very suitable for assisting language learning based on its own advantages. After a semester of multimodal teaching, the English teaching effectiveness of the university selected in this article has significantly improved. The research results indicate that the development of deep computer learning has introduced multimodal concepts into the teaching field, which is very suitable for assisting language learning based on its own advantages.