A high-frequency sense list

Abstract

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A number of high-frequency word lists have been created to help foreign language learners master English vocabulary. These word lists, despite their widespread use, did not take word meaning into consideration. Foreign language learners are unclear on which meanings they should focus on first. To address this issue, we semantically annotated the Corpus of Contemporary American English (COCA) and the British National Corpus (BNC) with high accuracy using a BERT model. From these annotated corpora, we calculated the semantic frequency of different senses and filtered out 5000 senses to create a High-frequency Sense List. Subsequently, we checked the validity of this list and compared it with established influential word lists. This list exhibits three notable characteristics. First, it achieves stable coverage in different corpora. Second, it identifies high-frequency items with greater accuracy. It achieves comparable coverage with lists like GSL, NGSL, and New-GSL but with significantly fewer items. Especially, it includes everyday words that used to fall off high-frequency lists without requiring manual adjustments. Third, it describes clearly which senses are most frequently used and therefore should be focused on by beginning learners. This study represents a pioneering effort in semantic annotation of large corpora and the creation of a word list based on semantic frequency.

Keywords

• BERT
• semantic annotation
• sense frequency
• word list
• large language model