A classroom-based study on the effectiveness of lexicographic resources

Machine-readable databases such as FrameNet (based on frame semantics) and WordNet (based on lexical semantic relations) appeared in the 1990s and became part of the lexicographic scene. The current study argues that FrameNet and WordNet can contribute to addressing the lexicographic challenge of sense delineation and elicit better performance from learners of English as a second language. The study examined the decoding and encoding performance of university students (n = 48) after exposure to modified lexicographic entries from FrameNet, WordNet, and the online Oxford Learner’s Dictionary. The classroom experiment assessed the accuracy of sense selection, user perplexity, and the accuracy of synonym production, and measured the response time for each question. An online survey followed the test, in order to collect further information about students’ dictionary use and evaluation of guide words and definitions. Results revealed significant intergroup differences in the response time, perplexity level, and encoding performance. Learners who consulted the modified FrameNet-based entries were the fastest and most successful among the three groups. Future studies can benefit from simplifying the name of frames in FrameNet and modifying the microstructure of the database for pedagogical purposes

FrameNet · Language model · Lexical Database · Lexicographical order · Linguistics · Natural language processing · Perplexity · Programming language · WordNet · Computer Science · Lexicography and Language Studies · Mathematics · Natural Language Processing Techniques · Psychology · Second Language Acquisition and Learning · Artificial Intelligence