Weakly-supervised discovery of named entities using web search queries
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Exploiting web search to generate synonyms for entities
Proceedings of the 18th international conference on World wide web
Understanding user's query intent with wikipedia
Proceedings of the 18th international conference on World wide web
Locating complex named entities in web text
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Web-scale distributional similarity and entity set expansion
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Open entity extraction from web search query logs
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Active objects: actions for entity-centric search
Proceedings of the 21st international conference on World Wide Web
Detecting candidate named entities in search queries
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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
Extraction and evaluation of candidate named entities in search engine queries
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
| Hi-index | 0.00 |
Query logs of a Web search engine have been increasingly used as a vital source for data mining. This paper presents a study on large-scale domain-independent entity extraction from search query logs. We present a completely unsupervised method to extract entities by applying pattern-based heuristics and statistical measures. We compare against existing techniques that use Web documents as well as search logs, and show that we improve over the state of the art. We also provide an in-depth qualitative analysis outlining differences and commonalities between these methods.