Latent Semantic Analysis has only recently been applied to textual entailment recognition. However, these efforts have suffered from inadequate bag of words vector representations. Our prototype implementation for the Third Recognising Textual Entailment Challenge (RTE-3) improves the approach by applying it to vector representations that contain semi-structured representations of words. It uses variable size n-grams of word stems to model independently verbs, subjects and objects displayed in textual statements. The system performance shows positive results and provides insights about how to improve them further.