Solving Vocabulary Matching Problems with Wordnet

Abstract

This study investigated the use of the lexical database WordNet to solve vocabulary matching quizzes. By using the relations in WordNet to match words with semantically similar definitions, it is possible to discover current deficiencies in WordNet, and by experimenting with different ways of using WordNet to find matches, some insight was gained into the semantic relations that tend to exist between words and definitions. Several different methods for measuring semantic similarity between words and definitions were tried and compared, including methods using the WordNet hypernym-hyponym hierarchy, methods using glosses, and methods using other relations in WordNet. Two different algorithms for matching given a set of similarity scores were explored: a greedy matching algorithm and a method that searches globally for the match with the maximum score. It was discovered that by scoring matches using paths through different WordNet relations, 82-85% of the words in the quizzes could be correctly matched, and the most useful relations were the hypernym and similar to relations. However, there were many word and definition pairs that were unable to be matched, revealing some places where WordNet could be improved.