Improving key concept extraction using word association measurement

Ontologies play a very important role in information exchange and sharing, and are typically constructed by human experts. However, this process is very costly in both time and effort. Given this, there is a need for automated ontology construction from various knowledge (such as text files). A key challenge of automated ontology learning from text is to extract key concepts, which are relevant to the domain, from the documents. Existing approaches typically require a large set of training data with prior domain-specific knowledge. However, it is not always possible to provide such knowledge and trained data sets. To overcome this issue, we present a method to obtain key concepts from unstructured texts by using the word association measure and statistical knowledge. To demonstrate the efficiency of our method in comparison with a state-of-the-art method, extensive experiments, which employed two real-world datasets, were performed. The obtained results indicate that our method achieves better accuracy than the state of the art method for 3% to 10% in case of not having domain-specific knowledge. The results are more efficient if there are many noun phrases (in data sets) whose number of words is large.