SEMILAR: A Semantic Similarity Toolkit for Assessing Students' Natural Language Inputs

We present in this demo SEMILAR, a SEMantic similarity toolkit. SEMILAR includes offers in one software environment several broad categories of semantic similarity methods: vectorial methods including Latent Semantic Analysis, probabilistic methods such as Latent Dirichlet Allocation, greedy lexical matching methods, optimal lexico-syntactic matching methods based on word-to-word similarities and syntactic dependencies with negation handling, kernel based methods, and some others. We will demonstrate during this demo presentation the efficacy of using SEMILAR to investigate and tune assessment algorithms for evaluating students’ natural language input based on data from the DeepTutor computer tutor.