AI-KU: Using Co-Occurrence Modeling for Semantic Similarity

In this paper, we describe our unsupervised method submitted to the Cross-Level Se-mantic Similarity task in Semeval 2014 that computes semantic similarity between two different sized text fragments. Our method models each text fragment by using the co-occurrence statistics of either occurred words or their substitutes. The co-occurrence mod-eling step provides dense, low-dimensional embedding for each fragment which allows us to calculate semantic similarity using various similarity metrics. Although our current model avoids the syntactic infor-mation, we achieved promising results and outperformed all baselines.