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

ACL ID S14-2011
Title AI-KU: Using Co-Occurrence Modeling for Semantic Similarity
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

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.