The literature contains a disconnect between accounts of how humans learn lexical semantic representations for words. Theories generally propose that lexical semantics are learned either through perceptual experience or through exposure to regularities in language. We propose here a model to integrate these two information sources. Specifically, the model uses the global structure of memory to ...

We describe the system we used at the PASCAL-2005 Recognizing Textual Entailment Challenge. Our method for recognizing entailment is based on calculating “directed” sentence similarity: checking the directed “semantic” word overlap between the text and the hypothesis. We use frequency-based term weighting in combination with two different lexical similarity measures. Although one version of the...

This paper investigates the appropriateness of using lexical cohesion analysis to assess Chinese readability. In addition to term frequency features, we derive features from the result of lexical chaining to capture the lexical cohesive information, where E-HowNet lexical database is used to compute semantic similarity between nouns with high word frequency. Classification models for assessing ...

Semantic Textual Similarity is the task of finding the degree of similarity between a pair of sentences through semantics extraction. This is motivated by the fact that syntactically diverse sentences often convey the same meaning. This paper describes the approach that was used in the *SEM Shared Task 2013. The approach combines semantic, syntactic and lexical similarity measures for finding s...