Mining query subtopics from search log data

Hu, Yunhua; Qian, Yanan; Li, Hang; Jiang, Daxin; Pei, Jian; Zheng, Qinghua

doi:10.1145/2348283.2348327

Cited by 54 publications

(34 citation statements)

References 29 publications

(32 reference statements)

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“…Yunhua Hu, Yanan Qian, Hang Li, Daxin Jiang, Jian Pei,and Qinghua Zheng proposes a method [6] for mining query subtopics from search log data. Identifying the major senses and angles of queries from search log data, referred to as query subtopic mining.…”

Section: Literature Reviewmentioning

confidence: 99%

A Survey on Mining Aspects for Queries

Padmanabhan¹,

D²

2017

International Journal of Advanced Research in Computer and Comm

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Abstract:There is a problem of finding query facets or angles that is a particular aspect or feature of something which are multiple groups of words or phrases that explain and summarize th content covered by a query. Query facets provide interesting and useful knowledge about a query and thus can be used to improve search experiences in many ways. First, we can display query facets together with the original search results in an appropriate way. Second, query facets may provide direct information or instant answers that users are seeking. Third, query facets may also be used to improve the diversity of the ten blue links. It is an assumption that the important aspects of a query are usually presented and repeated in the query's top retrieved documents in the style of lists, and query facets can be mined out by aggregating these significant lists. This can be solved by automatically mine query facets by extracting and grouping frequent lists from free text, HTML tags, and repeat regions within top search results. Then a large number of lists do exist and useful query angles can be mined.

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Section: Literature Reviewmentioning

confidence: 99%

A Survey on Mining Aspects for Queries

Padmanabhan¹,

D²

2017

International Journal of Advanced Research in Computer and Comm

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“…Wang and Zhai (2007) and Hu et al (2012) used related queries from search logs as candidates, and clustered them into query subtopics. Wang and Zhai (2007), for example, used snippets of a query's clicked web documents to enrich the query representation, and then cluster related past queries into query subtopics.…”

Section: Query Subtopic Miningmentioning

confidence: 99%

Extending Faceted Search to the Open-Domain Web

Kong

2016

SIGIR Forum

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To mom and dad ACKNOWLEDGMENTSI would like to express my deepest thanks to my advisor, James Allan, for his immense support and guidance throughout my PhD study. I especially thank James for giving me lots of freedom in research, which makes the years of PhD study a very enjoyable journey. In conventional faceted search, facets are generated in advance for an entire corpus either manually or semi-automatically, and then recommended for particular queries in most of the previous work. However, this approach is difficult to extend to the entire web due to the web's large and heterogeneous nature. We instead propose a querydependent approach, which extracts facets for queries from their web search results.We further improve our facet generation model under a more practical scenario, where users care more about precision of presented facets than recall.The dominant facet feedback method in conventional faceted search is Boolean filtering, which filters search results by users' selections on facets. However, our investigation shows Boolean filtering is too strict when extended to the open-domain setting. Thus, we propose soft ranking models for Faceted Web Search, which expand original queries with users' selections on facets to re-rank search results. Our experiments show that the soft ranking models are more effective than Boolean filtering models for Faceted Web Search.To evaluate Faceted Web Search, we propose both intrinsic evaluation, which evaluates facet generation on its own, and extrinsic evaluation, which evaluates an entire Faceted Web Search system by its utility in assisting search clarification. We also design a method for building reusable test collections for such evaluations. Our experiments show that using the Faceted Web Search interface can significantly improve the original ranking if allowed sufficient time for user feedback on facets.viii

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“…It can be represented as a set of terms that together describe the distinct information need [29,31,5] or as a single keyword that succinctly describes the topic [28]. Different resources have been used for mining query subtopics, including query logs [30,11,32,29,31,33], document corpus [2] and anchor texts [5].…”

Section: Query Subtopic/aspect Miningmentioning

confidence: 99%

Extracting query facets from search results

Kong

Allan

2013

Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Web search queries are often ambiguous or multi-faceted, which makes a simple ranked list of results inadequate. To assist information finding for such faceted queries, we explore a technique that explicitly represents interesting facets of a query using groups of semantically related terms extracted from search results. As an example, for the query "baggage allowance", these groups might be different airlines, different flight types (domestic, international), or different travel classes (first, business, economy). We name these groups query facets and the terms in these groups facet terms. We develop a supervised approach based on a graphical model to recognize query facets from the noisy candidates found. The graphical model learns how likely a candidate term is to be a facet term as well as how likely two terms are to be grouped together in a query facet, and captures the dependencies between the two factors. We propose two algorithms for approximate inference on the graphical model since exact inference is intractable. Our evaluation combines recall and precision of the facet terms with the grouping quality. Experimental results on a sample of web queries show that the supervised method significantly outperforms existing approaches, which are mostly unsupervised, suggesting that query facet extraction can be effectively learned.

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Mining query subtopics from search log data

Cited by 54 publications

References 29 publications

A Survey on Mining Aspects for Queries

A Survey on Mining Aspects for Queries

Extending Faceted Search to the Open-Domain Web

Extracting query facets from search results

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