Corpus Design of Chinese Medicine English Vocabulary Translation Teaching System Based on Python

Abstract

The current corpus has the problem of imperfect span retrieval function, which leads to a large classification noise. This paper designs a Python-based corpus of Chinese medicine English vocabulary translation teaching system. Here, we select the script material of web crawler, extract topic tags in the form of tag window, calculate the amount of information carried by words, use Python to extract the characteristics of Chinese medicine English vocabulary, and according to the observation value of exploration strategy, use instant time difference learning algorithm to construct the translation mode of teaching system, limit the scope of key words, and design the cross-range retrieval function of corpus. Experimental results: the average classification noise of the designed corpus and the other two corpora is 25.007[Formula: see text]dB, 33.877[Formula: see text]dB and 32.166[Formula: see text]dB, which proves that the integrated Python corpus has higher comprehensive value.