The extraction of multi-word relevant expressions has been an increasingly hot topic in the last few years. Relevant expressions are applicable in diverse areas such as Information Retrieval, document clustering, or classification and indexing of documents. However, relevant single-words, which represent much of the knowledge in texts, have been a relatively dormant field. In this paper we present a statistical language-independent approach to extract concepts formed by relevant single and multi-word units. By achieving promising precision/recall values, it can be an alternative both to language dependent approaches and to extractors that deal exclusively with multi-words.
