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 was due to be presented at the ISKO UK event Using knowledge organization to deliver content (which will hopefully be rescheduled later this year).

Read Full Post »

Advanced search needs advanced suggestions

Do you use search suggestions on Google? I’ll wager you do: they save time, help to minimise error-prone keystrokes, and even if not consciously processed, can give us the inspiration we need to form better queries than we’d originally contemplated.

But query suggestions, as we currently understand them, have their limitations. In particular, they are predicated on the assumption that most queries are composed of relatively short sequences of keywords, perhaps with some elementary linguistic structure. This is of course largely true for web search, but for many advanced or professional search applications, the assumption breaks down.

Read Full Post »