Clicked phrase document expansion for sponsored search ad retrieval

Hillard, Dustin; Leggetter, Chris

doi:10.1145/1835449.1835622

Cited by 2 publications

(1 citation statement)

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“…Li et al use query click logs to determine the domain of a query (typically keyword search queries), and then infer the class memberships of unlabeled queries from those of the labeled search queries using the URLs the users clicked Li et al, 2008). QCL have been used to extract named-entities to improve web search and ad publishing experience (Hillard and Leggetter, 2010) using (un)supervised learning methods on keyword based search queries. Different from previous re-search, in this paper we focus on recent research that utilize NL search queries to boost the performance of SLU components, i.e., domain detection, intent determination, and slot filling.…”

Section: Exploiting Nl Search Queries For Slumentioning

confidence: 99%

Employing web search query click logs for multi-domain spoken language understanding

Hakkani‐Tür

Tür

Heck

et al. 2011

2011 IEEE Workshop on Automatic Speech Recognition &Amp; Understanding

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Abstract-Logs of user queries from a search engine (such as Bing or Google) together with the links clicked provide valuable implicit feedback to improve statistical spoken language understanding (SLU) models. In this work, we propose to enrich the existing classification feature set for domain detection with features computed using the click distribution over a set of clicked URLs from search query click logs (QCLs) of user utterances. Since the form of natural language utterances differs stylistically from that of keyword search queries, to be able to match natural language utterances with related search queries, we perform a syntax-based transformation of the original utterances, after filtering out domain-independent salient phrases. This approach results in significant improvements for domain detection, especially when detecting the domains of web-related user utterances.

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Section: Exploiting Nl Search Queries For Slumentioning

confidence: 99%

Employing web search query click logs for multi-domain spoken language understanding

Hakkani‐Tür

Tür

Heck

et al. 2011

2011 IEEE Workshop on Automatic Speech Recognition &Amp; Understanding

Self Cite

View full text Add to dashboard Cite

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Modeling and solving term mismatch for full-text retrieval

Zhao

2012

SIGIR Forum

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Even though modern retrieval systems typically use a multitude of features to rank documents, the backbone for search ranking is usually the standard tf.idf retrieval models.This thesis addresses a limitation of the fundamental retrieval models, the term mismatch problem, which happens when query terms fail to appear in the documents that are relevant to the query. The term mismatch problem is a long standing problem in information retrieval. However, it was not well understood how often term mismatch happens in retrieval, how important it is for retrieval, or how it affects retrieval performance. This thesis answers the above questions, and proposes principled solutions to address this limitation. The new understandings of the retrieval models will benefit its users, as well as inform the development of software applications built on top of them.This new direction of research is enabled by the formal definition of the probability of term mismatch, and quantitative data analyses around it. In this thesis, term mismatch is defined as the probability of a term not appearing in a document that is relevant to the query. The complement of term mismatch is the term recall, the probability of a term appearing in relevant documents. Even though the term recall probability is known to be a fundamental quantity in the theory of probabilistic information retrieval, prior research in ad hoc retrieval provided few clues about how to estimate term recall reliably.This dissertation research designs two term mismatch prediction methods. With exploratory data analyses, this research first identifies common reasons that user-specified query terms fail to appear in documents relevant to the query, develops features correlated with each reason, and integrates them into a predictive model that can be trained from data. This prediction model uses training queries with relevance judgments to predict term mismatch for test queries without known relevance, and can be viewed as a type of transfer learning where training queries represent related ranking tasks that are used by the learning algorithm to facilitate the ranking for new test tasks. Further data analyses focus on the variation of the term mismatch probability for the same term across different queries, and demonstrate that query dependent features are needed for effective term mismatch prediction. At the same time, because the cross-query variation of term mismatch

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Clicked phrase document expansion for sponsored search ad retrieval

Cited by 2 publications

References 7 publications

Employing web search query click logs for multi-domain spoken language understanding

Employing web search query click logs for multi-domain spoken language understanding

Modeling and solving term mismatch for full-text retrieval

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