Measuring Semantic Similarity in Short Texts through Greedy Pairing and Word Semantics

We propose in this paper a greedy method to the problem of measuring semantic similarity between short texts. Our method is based on the principle of compositionality which states that the overall meaning of a sentence can be captured by summing up the meaning of its parts, i.e. the meanings of words in our case. Based on this principle, we extend word-to-word semantic similarity metrics to quantify the semantic similarity at sentence level. We report results using several word-to-word semantic similarity metrics, based on word knowledge or vectorial representations of meaning. Our approach performs better than similar approaches on the tasks of paraphrase identification and recognizing textual entailment, which are two illustrative semantic similarity tasks. We also report the role of word weighting and of function words on the performance of the proposed method.