A simple unsupervised latent semantics based approach for sentence similarity

The Semantic Textual Similarity (STS) shared task (Agirre et al., 2012) computes the degree of semantic equivalence between two sen-tences.1 We show that a simple unsupervised latent semantics based approach, Weighted Textual Matrix Factorization that only exploits bag-of-words features, can outperform most systems for this task. The key to the approach is to carefully handle missing words that are not in the sentence, and thus rendering it su-perior to Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). Our sys-tem ranks 20 out of 89 systems according to the official evaluation metric for the task, Pear-son correlation, and it ranks 10/89 and 19/89 in the other two evaluation metrics employed by the organizers.