ACM SIGIR Forum
The Journal of Machine Learning Research
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Language independent NER using a maximum entropy tagger
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Planning with Action Abstraction and Plan Decomposition Hierarchies
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Determining the user intent of web search engine queries
Proceedings of the 16th international conference on World Wide Web
Acquiring ontological knowledge from query logs
Proceedings of the 16th international conference on World Wide Web
Robust classification of rare queries using web knowledge
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Named entity recognition in query
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Automatically generating Wikipedia articles: a structure-aware approach
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Building taxonomy of web search intents for name entity queries
Proceedings of the 19th international conference on World wide web
A user behavior model for average precision and its generalization to graded judgments
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Towards query log based personalization using topic models
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Topic Pages: An Alternative to the Ten Blue Links
ICSC '10 Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing
Domain-independent entity extraction from web search query logs
Proceedings of the 20th international conference companion on World wide web
Jigs and lures: associating web queries with structured entities
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Clickthrough-based latent semantic models for web search
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Communications of the ACM
Unsupervised identification of synonymous query intent templates for attribute intents
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
On building entity recommender systems using user click log and freebase knowledge
Proceedings of the 7th ACM international conference on Web search and data mining
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We introduce an entity-centric search experience, called Active Objects, in which entity-bearing queries are paired with actions that can be performed on the entities. For example, given a query for a specific flashlight, we aim to present actions such as reading reviews, watching demo videos, and finding the best price online. In an annotation study conducted over a random sample of user query sessions, we found that a large proportion of queries in query logs involve actions on entities, calling for an automatic approach to identifying relevant actions for entity-bearing queries. In this paper, we pose the problem of finding actions that can be performed on entities as the problem of probabilistic inference in a graphical model that captures how an entity bearing query is generated. We design models of increasing complexity that capture latent factors such as entity type and intended actions that determine how a user writes a query in a search box, and the URL that they click on. Given a large collection of real-world queries and clicks from a commercial search engine, the models are learned efficiently through maximum likelihood estimation using an EM algorithm. Given a new query, probabilistic inference enables recommendation of a set of pertinent actions and hosts. We propose an evaluation methodology for measuring the relevance of our recommended actions, and show empirical evidence of the quality and the diversity of the discovered actions.