A Sememe Prediction Method Based on the Central Word of a Semantic Field

Abstract

A “sememe” is an indivisible minimal unit of meaning in linguistics. Manually annotating sememes in words requires a significant amount of time, so automated sememe prediction is often used to improve efficiency. Semantic fields serve as crucial mediators connecting the semantics between words. This paper proposes an unsupervised method for sememe prediction based on the common semantics between words and semantic fields. In comparison to methods based on word vectors, this approach demonstrates a superior ability to align the semantics of words and sememes. We construct various types of semantic fields through ChatGPT and design a semantic field selection strategy to adapt to different scenario requirements. Subsequently, following the order of word–sense–sememe, we decompose the process of calculating the semantic sememe similarity between semantic fields and target words. Finally, we select the word with the highest average semantic sememe similarity as the central word of the semantic field, using its semantic primes as the predicted result. On the BabelSememe dataset constructed based on the sememe knowledge base HowNet, the method of semantic field central word (SFCW) achieved the best results for both unstructured and structured sememe prediction tasks, demonstrating the effectiveness of this approach. Additionally, we conducted qualitative and quantitative analyses on the sememe structure of the central word.