Open University Chinese Language and Literature Teaching Model Based on NLP Technology and Mobile Edge Computing

Abstract

With the further improvement of comprehensive national strength, both cultural soft power and international status have increased significantly. The Chinese language has also become one of the languages eagerly studied in all countries in the world. However, the teaching resources of Chinese as a foreign language are far from enough to meet the rapid growth of learning needs. With the continuous improvement of the network environment of high-performance, low-latency, and high-bandwidth mobile edge computing, using computer network technology to assist Chinese language learning is an effective way to meet the needs. Under this circumstance, this paper proposed to design and build a Chinese language learning system based on NLP (natural language processing) technology. The built system modules could be roughly divided into three parts: the basic module of the system, the learning module, and the tool module combined with NLP technology. And, through the rational use of these three modules, it provided learners with the basic teaching of the Chinese language and also collected a large number of relevant documents on the Chinese language and culture through the system, which was more convenient for learners to learn. Research showed that the learning system basically met the learning needs. According to the data survey on the use of international students, more than 90% of the international students who have used the system believed that the content expressed by the learning system was clear and scientific. The results showed that the design of the Chinese learning system met the learning needs of learners.

Translation Algorithm of Proper Nouns in English-Chinese Translation Based on Lexical Knowledge Spectrum Map

“…Through techniques such as word embedding models like Word2Vec, GloVe, or contextualized embeddings like BERT, translation systems can encode semantic information about language constructs, facilitating accurate mapping between source and target languages [13]. Vector analysis helps in overcoming linguistic nuances and ambiguities by capturing semantic meaning and context, thus improving the quality and accuracy of translations [14]. Additionally, vector analysis aids in addressing challenges such as polysemy and synonymy, enabling translation systems to select appropriate translations based on contextual cues and linguistic patterns [15].…”