Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Context-aware query classification
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Named entity recognition in query
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Bootstrapped named entity recognition for product attribute extraction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
An approach for named entity recognition in poorly structured data
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Role-explicit query identification and intent role annotation
Proceedings of the 21st ACM international conference on Information and knowledge management
Extraction and evaluation of candidate named entities in search engine queries
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Unsupervised identification of synonymous query intent templates for attribute intents
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Recently, the problem of Named Entity Recognition in Query (NERQ) is attracting increasingly attention in the field of information retrieval. However, the lack of context information in short queries makes some classical named entity recognition (NER) algorithms fail. In this paper, we propose to utilize the search session information before a query as its context to address this limitation. We propose to improve two classical NER solutions by utilizing the search session context, which are known as Conditional Random Field (CRF) based solution and Topic Model based solution respectively. In both approaches, the relationship between current focused query and previous queries in the same session are used to extract novel context aware features. Experimental results on real user search session data show that the NERQ algorithms using search session context performs significantly better than the algorithms using only information of the short queries.