NLP techniques for automated query suggestions
A key task in formulating effective search strategies is the identification of appropriate keywords and controlled vocabulary terms. Interactive features such as query expansion can play a key role in supporting these tasks. In this presentation we investigate a variety of methods for interactive query expansion based on manually curated resources (e.g. ontologies and terminologies) and on distributional methods (e.g. unsupervised machine learning). The results demonstrate the utility of distributional models and the value of using ngram order to optimise precision and recall. This work is due to be presented at the ISKO UK event Using knowledge organization to deliver content.