A comparison of semantic similarity techniques for a corpus of CSCL chats

This paper analyzes the results obtained by computing the semantic similarity score between pairs of words using different WordNet-based and corpus-based techniques for a custom-built corpus of documents. The authors computed semantic similarity scores for all the pairs of the most frequent words from the documents contained in the corpus, which consisted of multi-party online chat conversations between students engaged in a Computer-Supported Collaborative Learning (CSCL) task. Differences between results provided by the two types of similarity measurement techniques were observed, but also between techniques of the same type. Results were compared to expected results provided by human experts in order to see what similarity measurement technique is better for our custom-built corpus of texts extracted from a CSCL task.