Eugene Agichtein is a person.
- Professional Homepage: http://www.mathcs.emory.edu/~eugene/
- DBLP Author Page: http://www.informatik.uni-trier.de/~ley/db/indices/a-tree/a/Agichtein:Eugene.html
- (Agichtein, 2006) ⇒ Eugene Agichtein. (2006). "Confidence Estimation Methods for Partially Supervised Relation Extraction." In: Proceedings of SIAM Conference on Data Mining (SDM 2006).
- (Agichtein, 2005) ⇒ Eugene Agichtein. (2005). "Scaling Information Extraction to Large Document Collections." In: IEEE Data Eng. Bull., 28(4).
- (Agichtein, 2005) ⇒ Eugene Agichtein. (2005). "Extracting Relations from Large Text Collections." PhD thesis, Columbia University, New York.
- CITED BY: 19 http://scholar.google.com/scholar?q=%22Extracting+relations+from+large+text+collections%22+2005
- ABSTRACT: A wealth of information is hidden within unstructured text. Often, this information can be beat exploited in structured or relational form, which is well suited for sophisticated query processing, for integration with relational database management systems, and for data mining. This thesis addresses two fundamental problems in extracting relations from large text collections: (1) portability: tuning extraction systems for new domains and (2) scalability: scaling up information extraction to large collections of documents. To address the first problem, we developed the Snowball information extraction system, a domain-independent system that learns to extract relations from unstructured text based on only a handful of user-provided example relation instances. Snowball can then be adapted to extract new relations with minimum human effort. Snowball improves the extraction accuracy by automatically evaluating the quality of both the acquired extraction patterns and the extracted relation instances. To address the second problem, we developed the QXtract system, which learns search engine queries that retrieve the documents that are relevant to a given information extraction system and extraction task. QXtract can dramatically improve the efficiency of the information extraction process, and provides a building block for extracting structured information and text data mining from the web at large.
- (Agichtein & Ganti, 2004) ⇒ Eugene Agichtein, and Venkatesh Ganti. (2004). "Mining Reference Tables for Automatic Text Segmentation." In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge Discovery and Data Mining (KDD 2004).
- (Eskin & Agichtein, 2004) ⇒ E. Eskin, and Eugene Agichtein. (2004). "Combining Text Mining and Sequence Analysis to Discover Protein Functional Regions." In: Pacific Symposium on Biocomputing, 9.
- (Yu & Agichtein, 2003) ⇒ Hong Yu, and Eugene Agichtein. (2003). "Extracting Synonymous Gene and Protein Terms from Biological Literature." In: Proceedings of the 11th International Conference on Intelligent Systems for Molecular Biology (ISMB-2003).
- (Agichtein & Gravano, 2000) ⇒ Eugene Agichtein, and Luis Gravano. (2000). "Snowball: Extracting Relations from Large Plain-Text Collections." In: Proceedings of the 5th ACM International Conference on Digital Libraries (DL-2000).
- (Borthwick & al, 1998) ⇒ Andrew Borthwick, John Sterling, Eugene Agichtein, and Ralph Grishman. (1998). "Exploiting Diverse Knowledge Sources via Maximum Entropy in Named Entity Recognition." In: Proceedings of the Sixth Workshop on Very Large Corpora.