Select, Link and Rank: Diversified Query Expansion and Entity Ranking Using Wikipedia

Krishnan, A.; Deepak, P; Ranu, Sayan; Mehta, Sameep

doi:10.1007/978-3-319-48740-3_11

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…In our WISE 2016 paper [18], we had proposed the SLR method. In this paper, we generalize SLR into a framework, and also develop another method based on the framework, SER, one targeted at exploiting pre-learned word embeddings.…”

Section: Extension From Wise 2016 Papermentioning

confidence: 99%

Leveraging semantic resources in diversified query expansion

et al. 2017

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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.

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Section: Extension From Wise 2016 Papermentioning

confidence: 99%

Leveraging semantic resources in diversified query expansion

et al. 2017

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“…Indicative features such as trigger words (Mr., Mrs. etc), grammar properties and prede ned pa erns are inconsistent or absent in most academic titles. Furthermore, NER techniques rely on rich contextual information and semantic structures of text [9,18]. Paper titles, on the other hand, are structured to be succinct, and lack context words.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Concept Categorization and Extraction from Scientific Document Titles

Krishnan

Ashok

Zhi

2017

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

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is paper studies the automated categorization and extraction of scienti c concepts from titles of scienti c articles, in order to gain a deeper understanding of their key contributions and facilitate the construction of a generic academic knowledgebase. Towards this goal, we propose an unsupervised, domain-independent, and scalable two-phase algorithm to type and extract key concept mentions into aspects of interest (e.g., Techniques, Applications, etc.). In the rst phase of our algorithm we propose PhraseType, a probabilistic generative model which exploits textual features and limited POS tags to broadly segment text snippets into aspect-typed phrases. We extend this model to simultaneously learn aspect-speci c features and identify academic domains in multi-domain corpora, since the two tasks mutually enhance each other. In the second phase, we propose an approach based on adaptor grammars to extract ne grained concept mentions from the aspect-typed phrases without the need for any external resources or human e ort, in a purely data-driven manner. We apply our technique to study literature from diverse scienti c domains and show signi cant gains over state-of-the-art concept extraction techniques. We also present a qualitative analysis of the results obtained.

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