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Computational explanation of “fiction text effectivity” for vocabularyimprovement: Corpus analyses using latent semantic analysis

Abstract

Previous studies have suggested that fiction book reading has a stronger positive effect on vocabulary developmentthan nonfiction. In this study, we examined this phenomenon in terms of word appearance information in fiction (story texts),nonfiction (explanation texts), and web text using latent semantic analysis (LSA). In a human experiment with Japanese under-graduates, we replicated fiction (story) text effectivity. Participants who often read story texts achieved the highest vocabularytest scores. Then, in a corpus experiment, we constructed a story text corpus, explanation text corpus, and web text corpus ofidentical size. Based on these corpora, we calculated the LSA similarities between words, and simulated answering the samevocabulary test as used in the human experiment. The corpus experiment demonstrated the nonfiction (explanation) text effec-tively, that is, the explanation corpus was the highest. The cause of discrepancy in the results and the educational implicationsof this study were also discussed.

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