An Empirical Study of Word Class Labeling of Total Reduplication Lex⁃ emes in New Century Chinese-English Dictionary ( 2nd edition)

the widely discussed study of word class categorization has a long history of more than 2000 years,which is known as the study of the“God Particles”in language.As a typical analytic language,Modern Chinese,due to its lack of morphological changes,is challenged by a thorny problem of word classes especially when it comes to the criteria for word class identification and the treat⁃ment of multiple class membership.As such,all the controversies eventually give rise to some contradiction and confusion in word class labeling in Modern Chinese and Chinese-English dictionaries.As an important grammatical means in Chinese and the focus of lexicology and rhetorics,total reduplication lexemes serve as an essential part of Chinese-English dictionaries with complex and diverse word classes.Guided by the Two-level Word Class Categorization Theory,this thesis focuses on the word class labeling of total reduplication lexemes in New Century Chinese-English Dictionary(2nd edition)backed by large-scale balanced Modern Chi⁃nese corpora.With an innovative theoretical perspective,this study not only contributes to the word class labeling of total reduplica⁃tion lexemes and even sheds light on the compilation of Chinese-English dictionaries,but also drives the study of Modern Chinese word classes in the long term.

Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis

Twitter has emerged as a platform that produces new data every day through its users which can be utilized for various purposes.People express their unique ideas and views onmultiple topics thus providing vast knowledge.Sentiment analysis is critical from the corporate and political perspectives as it can impact decision-making.Since the proliferation of COVID-19,it has become an important challenge to detect the sentiment of COVID-19-related tweets so that people’s opinions can be tracked.The purpose of this research is to detect the sentiment of people regarding this problem with limited data as it can be challenging considering the various textual characteristics that must be analyzed.Hence,this research presents a deep learning-based model that utilizes the positives of random minority oversampling combined with class label analysis to achieve the best results for sentiment analysis.This research specifically focuses on utilizing class label analysis to deal with the multiclass problem by combining the class labels with a similar overall sentiment.This can be particularly helpful when dealing with smaller datasets.Furthermore,our proposed model integrates various preprocessing steps with random minority oversampling and various deep learning algorithms including standard deep learning and bi-directional deep learning algorithms.This research explores several algorithms and their impact on sentiment analysis tasks and concludes that bidirectional neural networks do not provide any advantage over standard neural networks as standard Neural Networks provide slightly better results than their bidirectional counterparts.The experimental results validate that our model offers excellent results with a validation accuracy of 92.5%and an F1 measure of 0.92.