Leveraging semantic resources in diversified query expansion

Abstract

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, SelectEmbed-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.

Semantics-based key concepts identification for documents indexing and retrieval on the web

“…Such corresponding entities are then added to the index structure assuming more relevant documents will be retrieved accordingly. Several studies have investigated and confirmed the impact of employing semantic resources on improving the quality of the indexing and retrieval process (Krishnan et al, 2018;Hyvönen and Rantala, 2019). However, we argue that existing semantic recourses still suffer from a number of limitations that hinder their actual exploitation in practical application domains.…”

Unsupervised Fake News Detection

“…We are considering ways in which this binary labelling can be relaxed in order to offer more fine-grained labellings, staying within the graph-based framework. Other directions include generating interpretable results [3] and enriching semantic query expansion (e.g., [19]) with veracity orientation.…”

Efficient Maximal Balanced Clique Enumeration in Signed Networks

“…After that, we decrease the corresponding edge common neighbor numbers that have been changed due to the removal of (u, v) for the edge incident to (u, v) (line 10-15) based on Definition 6.6. Similarly, for each negative edge not satisfying the conditions in Lemma 6.7, we remove it and decrease the corresponding edge common neighbor numbers (line [17][18][19][20][21][22][23][24]. The algorithm terminates when all the edges satisfy conditions in Lemma 6.7.…”

“…In such signed networks, our model can discover synonym groups that are antonymous with each other, such as, {interior, internal, intimate} and {away, foreign, outer, outside, remote}. These discovered groups may be further used in applications such as automatic question generation [24] and semantic expansion [22]. Contributions.…”