1 Introduction

Academic English vocabulary is crucial for academic communication and achievement, and vocabulary development is a research hotspot. Since the 21st century, general and professional academic word lists have been constantly updated, but there are differences between word lists due to differences in corpora, word counting units, and word selection standards. The main difference in word selection criteria is whether to include common vocabulary. AWL and AVL handle common vocabulary differently, with the former removing high-frequency common words and the latter retaining them. Whether the academic vocabulary list includes general vocabulary should be thoroughly examined for its meaning and usage in academic discourse. There are differences in the collocation and function of high-frequency generic words between academic discourse and generic discourse, and learners should be guided to pay attention in teaching. There is little research on existing academic word lists, and newly developed specialized word lists no longer exclude high-frequency general vocabulary. It is necessary to verify whether the high-frequency general vocabulary included in the academic vocabulary has specific meaning and usage in academic discourse.

2 Literature Review

Academic vocabulary validation is a key step in ensuring its effectiveness, typically achieved by detecting the coverage of the vocabulary in large-scale corpora. However, relying on word frequency may lead to the accidental deletion of general vocabulary with special academic significance. To address this issue, researchers have proposed various methods, including manual confirmation, word vector algorithm models, bilingual parallel corpora, and measuring the applicability of word lists by testing learners' vocabulary mastery. The co selection theory provides a solution for developing and validating academic word lists from the perspective of deep knowledge of vocabulary, emphasizing the integration of form, meaning, and function. The application of this theory helps to accurately select representative vocabulary that learners need to pay attention to and comprehensively describe the depth of vocabulary knowledge they need to master. Although the co selection theory has shown practical value in academic English teaching, it has not been fully utilized in the development of academic vocabulary and teaching utilization. Existing research has mostly focused on the co selection characteristics of sporadic vocabulary, and further research is needed to explore the application of co selection theory in academic vocabulary research. In addition, the co selection features of adjectives and other actual words in academic vocabulary, such as collocation, class connection, and semantic tendency, are in-depth knowledge that learners need to master. The description of these features is crucial for verifying whether vocabulary should be included in academic vocabulary.

3 Research Methods

This chapter adopts the co selection theory path, taking the adjective "common" as an example, to analyze its co selection characteristics in both non academic and academic terminology texts. The research subject is "common", which ranks 479 in the BNC word element table and is also included in AVL, MAVL, and FWL. The corpus sources include BNC non academic corpus, MedDEAP medical academic corpus, and EconDEAP economics academic corpus. Data collection was conducted by searching for the co-occurrence of "common" in three corpora and randomly selecting 200 rows of samples for analysis. The data analysis adopts the inductive method to summarize the co selection characteristics of "common" in three categories: collocation words, class connections, and semantic trends. Through two annotations, summarize the 8 structures and 4 semantics of "common", including the "common", "common", and "vulgar" meanings of adjectives, as well as the "common" and "land" meanings of nouns.

4 Results and Analysis

In the BNC non ac, MedDEAP, and EconDEAP corpora, the frequency of using the word 'common' in academic discourse is significantly higher than in non academic discourse. In BNC non ac, the co-occurrence sample of "common" shows that the ADJ-n structure is the most common, involving multiple semantic types, including "USUAL", "SHARED", and "LOW CLASS". In MedDEAP, the co-occurrence samples of "common" are mainly concentrated in two structures: "ADJ-n [USUAL]" and "n-link-v-ADJ - (pp) [USUAL]", while in EconDEAP, the distribution of co-occurrence samples is more dispersed, but the number of "ADJ-n [SHARED]" structures is the highest. Through comparative analysis, it was found that there are significant differences in the structure and semantic co selection distribution of "common" among the three corpora. The high-frequency co selection forms of "common" in academic corpora also have a high frequency in non academic terminology texts, indicating that these co selection forms do not pose additional reading or comprehension difficulties for learners. Therefore, it is not necessary to include "common" in academic vocabulary, but co selection features that learners need to pay attention to should be provided to guide them to focus on the typical meaning and usage of the word in academic discourse. The development of academic word lists should further identify phrases that co occur with high-frequency vocabulary, as phrases have clearer semantic and pragmatic information.

5 Conclusion

Academic word lists have a guiding role in teaching, but selecting words solely based on quantitative indicators may not be accurate. The co selection theory helps to verify the specialized meaning and usage of vocabulary by analyzing its collocation, class connection, and semantic tendency in academic discourse. Taking "common" as an example, research has shown that it is not particularly necessary to include it in academic discourse in medicine and economics. Whether to include high-frequency generic words should be determined based on their co selection characteristics in the scientific terminology section. The vocabulary validation method proposed in this article helps to screen key vocabulary and determine focus points. In the future, automated programs can be developed to improve the efficiency of vocabulary validation.