Measuring Similarity from Word Pair Matrices with Syntagmatic and Paradigmatic Associations

Two types of semantic similarity are usually distinguished: attributional and relational similarities. These similarities measure the degree between words or word pairs. Attributional similarities are bidrectional, while relational similarities are one-directional. It is possible to compute such similarities based on the occurrences of words in actual sentences. Inside sentences, syntagmatic associations and paradigmatic associations can be used to characterize the relations between words or word pairs. In this paper, we propose a vector space model built from syntagmatic and paradigmatic associations to measure relational similarity between word pairs from the sentences contained in a small corpus. We conduct two experiments with different datasets: SemEval-2012 task 2, and 400 word analogy quizzes. The experimental results show that our proposed method is effective when using a small corpus.