Modern Information Retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGIR Forum
The Journal of Machine Learning Research
Efficient query evaluation using a two-level retrieval process
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Formal models for expert finding in enterprise corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
An exploration of proximity measures in information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Proximity-based document representation for named entity retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Determining the informational, navigational, and transactional intent of Web queries
Information Processing and Management: an International Journal
Proceedings of the 25th international conference on Machine learning
Inferring the most important types of a query: a semantic approach
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A language modeling framework for expert finding
Information Processing and Management: an International Journal
Statistical Language Models for Information Retrieval A Critical Review
Foundations and Trends in Information Retrieval
Foundations and Trends in Databases
Understanding user's query intent with wikipedia
Proceedings of the 18th international conference on World wide web
Learning structural SVMs with latent variables
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Collective annotation of Wikipedia entities in web text
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Named entity recognition in query
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Two-stage query segmentation for information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Towards rich query interpretation: walking back and forth for mining query templates
Proceedings of the 19th international conference on World wide web
Understanding queries in a search database system
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Expressive and flexible access to web-extracted data: a keyword-based structured query language
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Structured annotations of web queries
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
How good is a span of terms?: exploiting proximity to improve web retrieval
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Web-scale entity-relation search architecture
Proceedings of the 20th international conference companion on World wide web
Facet discovery for structured web search: a query-log mining approach
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Learning models for ranking aggregates
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
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
Robust disambiguation of named entities in text
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Compressed data structures for annotated web search
Proceedings of the 21st international conference on World Wide Web
A ranking framework for entity oriented search using Markov random fields
Proceedings of the 1st Joint International Workshop on Entity-Oriented and Semantic Search
Mining entity types from query logs via user intent modeling
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Natural language questions for the web of data
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Interpreting keyword queries over web knowledge bases
Proceedings of the 21st ACM international conference on Information and knowledge management
Hierarchical target type identification for entity-oriented queries
Proceedings of the 21st ACM international conference on Information and knowledge management
Typing candidate answers using type coercion
IBM Journal of Research and Development
Hi-index | 0.00 |
Thanks to information extraction and semantic Web efforts, search on unstructured text is increasingly refined using semantic annotations and structured knowledge bases. However, most users cannot become familiar with the schema of knowledge bases and ask structured queries. Interpreting free-format queries into a more structured representation is of much current interest. The dominant paradigm is to segment or partition query tokens by purpose (references to types, entities, attribute names, attribute values, relations) and then launch the interpreted query on structured knowledge bases. Given that structured knowledge extraction is never complete, here we choose a less trodden path: a data representation that retains the unstructured text corpus, along with structured annotations (mentions of entities and relationships) on it. We propose two new, natural formulations for joint query interpretation and response ranking that exploit bidirectional flow of information between the knowledge base and the corpus. One, inspired by probabilistic language models, computes expected response scores over the uncertainties of query interpretation. The other is based on max-margin discriminative learning, with latent variables representing those uncertainties. In the context of typed entity search, both formulations bridge a considerable part of the accuracy gap between a generic query that does not constrain the type at all, and the upper bound where the "perfect" target entity type of each query is provided by humans. Our formulations are also superior to a two-stage approach of first choosing a target type using recent query type prediction techniques, and then launching a type-restricted entity search query.