Search Result Diversification Based on Hierarchical Intents

Hu, Sha; Dou, Zhicheng; Wang, Xiaojie; Sakai, Tetsuya; Wen, Ji-Rong

doi:10.1145/2806416.2806455

Cited by 67 publications

(32 citation statements)

References 36 publications

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“…In contrast, as introduced in Section 4.1, each of the aspects identified for the target type of q is ultimately represented by multiple labels with correlated semantics (e.g., 'epicentre', 'latitude', 'longitude'). Notably, hierarchical aspect modeling has recently been shown to provide a more accurate representation of the interdependencies among semantically correlated aspects, leading to a significant improvement in the resulting diversification effectiveness [20]. While a full hierarchical modeling of event aspects is beyond the scope of this article, we devise an extension of xQuAD in Equation (2) to model dependencies among the various labels L representing each aspect a ∈ A underlying the query q, according to:…”

Section: Explicit Diversification Of Event Aspectsmentioning

confidence: 99%

“…Second, analyzing the remaining events we see that xQuAS using the crowdsourced aspects performs more consistently well across those events than the same model using Wikipedia-based aspects. More precisely, using the crowdsourced labels, performance across events is 0.1 or better under the Combined metric with 4 exceptions (events 6,18,20,23). Contrast this to the same model using the Wikipedia-based aspects, where 8 events have less than 0.1 performance under the Combined metric (events 5,11,14,18,20,27,35,36).…”

Section: Comparing Event Aspect Representationsmentioning

confidence: 99%

“…More precisely, using the crowdsourced labels, performance across events is 0.1 or better under the Combined metric with 4 exceptions (events 6,18,20,23). Contrast this to the same model using the Wikipedia-based aspects, where 8 events have less than 0.1 performance under the Combined metric (events 5,11,14,18,20,27,35,36). Very low performances for a particular event indicates that the generated aspects for that event's type were not sufficiently descriptive, leading to relevant content being missed.…”

Section: Comparing Event Aspect Representationsmentioning

confidence: 99%

See 2 more Smart Citations

Explicit Diversification of Event Aspects for Temporal Summarization

McCreadie

Santos

Macdonald

et al. 2018

ACM Trans. Inf. Syst.

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During major events, such as emergencies and disasters, a large volume of information is reported on newswire and social media platforms. Temporal summarization (TS) approaches are used to automatically produce concise overviews of such events, by extracting text snippets from related articles over time. Current TS approaches rely on a combination of event relevance and textual novelty for snippet selection. However, for events that span multiple days, textual novelty is often a poor criterion for selecting snippets, since many snippets are textually unique, but are semantically redundant or non-informative. In this article, we propose a framework for the diversification of snippets using explicit event aspects, building upon recent works in search result diversification. In particular, we first propose two techniques to identify explicit aspects that a user might want to see covered in a summary for different types of event. We then extend a state-of-theart explicit diversification framework to maximize the coverage of these aspects when selecting summary snippets for unseen events. Through experimentation over the TREC TS 2013, 2014 and 2015 datasets, we show that explicit diversification for temporal summarization significantly outperforms classical noveltybased diversification, as the use of explicit event aspects reduces the amount of redundant and off-topic snippets returned, while also increasing summary timeliness.

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Section: Explicit Diversification Of Event Aspectsmentioning

confidence: 99%

Section: Comparing Event Aspect Representationsmentioning

confidence: 99%

Section: Comparing Event Aspect Representationsmentioning

confidence: 99%

See 1 more Smart Citation

Explicit Diversification of Event Aspects for Temporal Summarization

McCreadie

Santos

Macdonald

et al. 2018

ACM Trans. Inf. Syst.

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“…Two-level hierarchical intents based search result diversification methods were also proposed [12]. The method, proposed by Kim et al [28] was to mine a two-level subtopic hierarchy based on hierarchical search intentions.…”

Section: Related Workmentioning

confidence: 99%

“…Several methods were proposed for mining subtopics from different resources including the top retrieved documents, anchor text, query logs, Wikipedia, Freebase [9], and the related search services provided by the commercial search engines [4], [10], [11]. Query suggestions provided by commercial search engines hold some intents [10], [12]; however, suggested queries are often noisy and contain a group of similar suggestions covering a single aspect of the original query. Since both query and subtopics are short in length, it is challenging to efficiently estimate the similarity between a pair of short texts and rank them accordingly.…”

Section: Introductionmentioning

confidence: 99%

A Bipartite Graph-Based Ranking Approach to Query Subtopics Diversification Focused on Word Embedding Features

Ullah

Aono

2016

IEICE Trans. Inf. & Syst.

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SUMMARYWeb search queries are usually vague, ambiguous, or tend to have multiple intents. Users have different search intents while issuing the same query. Understanding the intents through mining subtopics underlying a query has gained much interest in recent years. Query suggestions provided by search engines hold some intents of the original query, however, suggested queries are often noisy and contain a group of alternative queries with similar meaning. Therefore, identifying the subtopics covering possible intents behind a query is a formidable task. Moreover, both the query and subtopics are short in length, it is challenging to estimate the similarity between a pair of short texts and rank them accordingly. In this paper, we propose a method for mining and ranking subtopics where we introduce multiple semantic and content-aware features, a bipartite graph-based ranking (BGR) method, and a similarity function for short texts. Given a query, we aggregate the suggested queries from search engines as candidate subtopics and estimate the relevance of them with the given query based on word embedding and content-aware features by modeling a bipartite graph. To estimate the similarity between two short texts, we propose a Jensen-Shannon divergence based similarity function through the probability distributions of the terms in the top retrieved documents from a search engine. A diversified ranked list of subtopics covering possible intents of a query is assembled by balancing the relevance and novelty. We experimented and evaluated our method on the NTCIR-10 INTENT-2 and NTCIR-12 IMINE-2 subtopic mining test collections. Our proposed method outperforms the baselines, known related methods, and the official participants of the INTENT-2 and IMINE-2 competitions.

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Explicit diversification of search results across multiple dimensions for educational search

Yigit-Sert

Altıngövde

Macdonald

et al. 2020

Asso for Info Science & Tech

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Making use of search systems to foster learning is an emerging research trend known as search as learning. Earlier works identified result diversification as a useful technique to support learning‐oriented search, since diversification ensures a comprehensive coverage of various aspects of the queried topic in the result list. Inspired by this finding, first we define a new research problem, multidimensional result diversification, in the context of educational search. We argue that in a search engine for the education domain, it is necessary to diversify results across multiple dimensions, that is, not only for the topical aspects covered by the retrieved documents, but also for other dimensions, such as the type of the document (e.g., text, video, etc.) or its intellectual level (say, for beginners/experts). Second, we propose a framework that extends the probabilistic and supervised diversification methods to take into account the coverage of such multiple dimensions. We demonstrate its effectiveness upon a newly developed test collection based on a real‐life educational search engine. Thorough experiments based on gathered relevance annotations reveal that the proposed framework outperforms the baseline by up to 2.4%. An alternative evaluation utilizing user clicks also yields improvements of up to 2% w.r.t. various metrics.

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Search Result Diversification Based on Hierarchical Intents

Cited by 67 publications

References 36 publications

Explicit Diversification of Event Aspects for Temporal Summarization

Explicit Diversification of Event Aspects for Temporal Summarization

A Bipartite Graph-Based Ranking Approach to Query Subtopics Diversification Focused on Word Embedding Features

Explicit diversification of search results across multiple dimensions for educational search

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