Leveraging semantic resources in diversified query expansion

A search query, being a very concise grounding of user intent, could potentially have many possible interpretations. Search engines hedge their bets by diversifying top results to cover multiple such possibilities so that the user is likely to be satisfied, whatever be her intended interpretation. Diversified Query Expansion is the problem of diversifying query expansion suggestions, so that the user can specialize the query to better suit her intent, even before perusing search results. In this paper, we consider the usage of semantic resources and tools to arrive at improved methods for diversified query expansion. In particular, we develop two methods, those that leverage Wikipedia and pre-learnt distributional word embeddings respectively. Both the approaches operate on a common three-phase framework; that of first taking a set of informative terms from the search results of the initial query, then building a graph, following by using a diversity-conscious node ranking to prioritize candidate terms for diversified query expansion. Our methods differ in the second phase, with the first method Select-Link-Rank (SLR) linking terms with Wikipedia entities to accomplish graph construction; on the other hand, our second method, Select-Embed-Rank (SER), constructs the graph using similarities between distributional word embeddings. Through an empirical analysis and user study, we show that SLR ourperforms state-of-the-art diversified query expansion methods, thus establishing that Wikipedia is an effective resource to aid diversified query expansion. Our empirical analysis also illustrates that SER outperforms the baselines convincingly, asserting that it is the best available method for those cases where SLR is not applicable; these include narrow-focus search systems where a relevant knowledge base is unavailable. Our SLR method is also seen to outperform a state-of-the-art method in the task of diversified entity ranking. <br/

What to Read Next? Challenges and Preliminary Results in Selecting Representative Documents

The vast amount of scientific literature poses a challenge when one is trying to understand a previously unknown topic. Selecting a representative subset of documents that covers most of the desired content can solve this challenge by presenting the user a small subset of documents. We build on existing research on representative subset extraction and apply it in an information retrieval setting. Our document selection process consists of three steps: computation of the document representations, clustering, and selection of documents. We implement and compare two different document representations, two different clustering algorithms, and three different selection methods using a coverage and a redundancy metric. We execute our 36 experiments on two datasets, with 10 sample queries each, from different domains. The results show that there is no clear favorite and that we need to ask the question whether coverage and redundancy are sufficient for evaluating representative subsets