Research on Features of Chatroom Netspeak From a Stylistic View

With the development and popularization of Internet, the appearance of computer mediated communication (CMC) has generated a new variety of language—netspeak, which received a wide publicity in modern linguistics. This paper focuses on the features of netspeak. Firstly, it gives an introduction of its background. Secondly, it discusses its features by employing a lot of examples from English and Chinese. In this part, this paper makes the analysis from the view of stylistics, focusing on phonological, lexical, syntactical and discoursal aspects. Special mention is given here to some differences and similarities found in English netspeak and Chinese netspeak. Thirdly, the great influence of netspeak on written language is involved, which reflects in morphology, meaning, grammar and the degree of politeness. Finally, a conclusion of the features of netspeak is given as well as a reasonable anticipation of its tendency. Lacking in the knowledge of the stylistic features of netspeak, chitchat on line will result in failure in communication. Therefore, this paper, through the systematic analysis on netspeak, aims at revealing its distinctive features and getting netizens to communicate better. As the cyber culture is evolving, netspeak is also changing, which will generate more new features. The study on netspeak needs further analysis and it is never ended

Sample Mixed-Based Data Augmentation for Domestic Audio Tagging

Audio tagging has attracted increasing attention since last decade and has various potential applications in many fields. The objective of audio tagging is to predict the labels of an audio clip. Recently deep learning methods have been applied to audio tagging and have achieved state-of-the-art performance, which provides a poor generalization ability on new data. However due to the limited size of audio tagging data such as DCASE data, the trained models tend to result in overfitting of the network. Previous data augmentation methods such as pitch shifting, time stretching and adding background noise do not show much improvement in audio tagging. In this paper, we explore the sample mixed data augmentation for the domestic audio tagging task, including mixup, SamplePairing and extrapolation. We apply a convolutional recurrent neural network (CRNN) with attention module with log-scaled mel spectrum as a baseline system. In our experiments, we achieve an state-of-the-art of equal error rate (EER) of 0.10 on DCASE 2016 task4 dataset with mixup approach, outperforming the baseline system without data augmentation.Comment: submitted to the workshop of Detection and Classification of Acoustic Scenes and Events 2018 (DCASE 2018), 19-20 November 2018, Surrey, U

On the Role of “Fuzzy Language” in Cultivating “Core Accomplishment”: Based on the Cross-Cultural Communication

Fuzzy language has something to do with beauty, techniques, competence and accomplishment. On the way from "core knowledge era" to "core accomplishment era", the author tries to focus on the role of fuzzy language in cultivating students’ core accomplishment by cultivating aesthetic taste, promoting international understanding, enriching humanistic accomplishment and enhancing practical ability