Oren Etzioni
- Agichtein, E. and Gravano, L. Snowball: Extracting relations from large plain-text collections. In Proceedings of the 5th ACM International Conference on Digital Libraries (2000). Google Scholar
Digital Library
- ARPA. Proceedings of the 3rd Message Understanding Conference (1991).Google Scholar
- Banko, M., Cafarella, M., Soderland, S., Broadhead, M. and Etzioni, O. Open information extraction from the Web. In Proceedings of the International Joint Conference on Artificial Intelligence (2007). Google ScholarDigital Library
- Banko, M. and Etzioni, O. The tradeoffs between traditional and open relation extraction. In Proceedings of the Association of Computational Linguistics (2008).Google Scholar
- Brin, S. Extracting patterns and relations from the World Wide Web. In Proceedings of the Workshop at the 6th International Conference on Extending Database Technology, (Valencia, Spain, 1998), 172--183. Google ScholarDigital Library
- Bunescu, R. and Mooney, R. Learning to extract relations from the Web using minimal supervision In Proceedings of the Association of Computational Linguistics (2007).Google Scholar
- Downey, D., Etzioni, O. and Soderland, S. A probabilistic model of redundancy in information extraction. In Proceedings of the International Joint Conference on Artificial Intelligence (2005). Google ScholarDigital Library
- Downey, D., Schoenmackers, S. and Etzioni, O. Sparse information extraction: Unsupervised language models to the rescue. In Proceedings of the Association of Computational Linguistics (2007).Google ScholarCross Ref
- Etzioni, O., Cafarella, M., Downey, D., Kok, S., Popescu, A., Shaked, T., Soderland, S., Weld, D. and Yates, A. Unsupervised named-entity extraction from the Web: An experimental study. Artificial Intelligence 165,1 (2005), 91--134. Google ScholarDigital Library
- Feldman, R., Rosenfeld, B., Soderland, S. and Etzioni, O. Self-supervised relation extraction from the Web. In Proceedings of the International Symposium on Methodologies for Intelligent Systems (2006) 755--764. Google ScholarDigital Library
- Kim, J. and Moldovan, D. Acquisition of semantic patterns for information extraction from corpora. In Proceedings of the 9th IEEE Conference on Artificial Intelligence for Applications (1993), 171--176.Google ScholarCross Ref
- Lafferty, J., McCallum, A. and Pereira, F. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the 2001 International Conference on Machine Learning. Google ScholarDigital Library
- McCallum, A. Efficiently inducing features of conditional random fields. In Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence (Acapulco, 2003), 403--410. Google ScholarDigital Library
- Poon, H. and Domingos, P. Joint inference in information extraction. In Proceedings of the 22nd National Conference on Artificial Intelligence (2007) 913--918. Google ScholarDigital Library
- Popescu, A. and Etzioni, O. Extracting product features and opinions from reviews. In Proceedings of the Empirical Methods on Natural Language Processing Conference (2005). Google ScholarDigital Library
- Popescu. A-M. Information extraction from unstructured Web text. Ph.D. thesis, University of Washington (2007). Google ScholarDigital Library
- Riloff, E. Automatically constructing extraction patterns from untagged text. In Proceedings of the 13th National Conference on Artificial Intelligence (1996), 1044--1049. Google ScholarDigital Library
- Riloff, E. and Jones, R. Learning dictionaries for information extraction by multi-level bootstrapping. In Proceedings of the AAAI-99 Conference (1999), 1044--1049. Google ScholarDigital Library
- Schubert, L. Can we derive general world knowledge from texts? In Proceedings of the Human Language Technology Conference (2002). Google ScholarDigital Library
- Shinyama, Y. and Sekine, S. Preemptive information extraction using unrestricted relation discovery. In Proceedings of the Human Language Technology/NAACL Conference (2006). Google ScholarDigital Library
- Soderland, S. Learning information extraction rules for semi-structured and free text. Machine Learning 34, 1--3(1999), 233--272. Google ScholarDigital Library
- Soderland, S., Fisher, D., Aseltine, J. and Lehnert, W. CRYSTAL: Inducing a conceptual dictionary. In Proceedings of the 14th International Joint Conference on Artificial Intelligence (1995), 1314--1321. Google ScholarDigital Library
- Weld, D., Wu, F., Adar, E., Amershi, S., Fogarty, J., Hoffmann, R., Patel, K. and Skinner, M. Intelligence in Wikipedia. In Proceedings of the 23rd Conference on Artificial Intelligence (2008). Google ScholarDigital Library
- Yates, A. and Etzioni, O. Unsupervised resolution of objects and relations on the Web. In Proceedings of the Human-Language Technology Conference (2007).Google Scholar
Index Terms
- Open information extraction from the web
Recommendations
Open information extraction from the web
IJCAI'07: Proceedings of the 20th international joint conference on Artifical intelligenceTraditionally, Information Extraction (IE) has focused on satisfying precise, narrow, pre-specified requests from small homogeneous corpora (e.g., extract the location and time of seminars from a set of announcements). Shifting to a new domain requires ...
A weighting scheme for open information extraction
NAACL HLT '12: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research WorkshopWe study the problem of extracting all possible relations among named entities from unstructured text, a task known as Open Information Extraction (Open IE). A state-of-the-art Open IE system consists of natural language processing tools to identify ...
Web-based open-domain information extraction
CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge managementThis tutorial provides an overview of extraction methods developed in the area of Web-based open-domain information extraction, whose purpose is the acquisition of open-domain classes, instances and relations from Web text. The extraction methods ...
Comments