%0 Conference Paper %1 oai:CiteSeerPSU:241567 %A Miller, Scott %A Crystal, Michael %A Fox, Heidi %A Ramshaw, Lance %A Schwartz, Richard %A Stone, Rebecca %A Weischedel, Ralph %A Group, The Annotation %B MUC-7 %D 1998 %K imported %T Algorithms That Learn To Extract Information BBN: Description Of The Sift System As Used For MUC-7 %U http://acl.ldc.upenn.edu/muc7/M98-0009.pdf %X For MUC-7, BBN has for the first time fielded a fully-trained system for NE, TE, and TR; results are all the output of statistical language models trained on annotated data, rather than programs executing handwritten rules. Such trained systems have some significant advantages: . They can be easily ported to new domains by simply annotating data with semantic answers. . The complex interactions that make rule-based systems difficult to develop and maintain can here be learned automatically from the training data. We believe that the results in this evaluation are evidence that such trained systems, even at their current level of development, can perform roughly on a par with rules hand-tailored by experts. Since MUC-3, BBN has been steadily increasing the proportion of the information extraction process that is statistically trained. Already in MET-1, our name-finding results were the output of a fully statistical, HMM-based model, and that statistical Identifinder^TM model was als...

@inproceedings{oai:CiteSeerPSU:241567, abstract = {For MUC-7, BBN has for the first time fielded a fully-trained system for NE, TE, and TR; results are all the output of statistical language models trained on annotated data, rather than programs executing handwritten rules. Such trained systems have some significant advantages: . They can be easily ported to new domains by simply annotating data with semantic answers. . The complex interactions that make rule-based systems difficult to develop and maintain can here be learned automatically from the training data. We believe that the results in this evaluation are evidence that such trained systems, even at their current level of development, can perform roughly on a par with rules hand-tailored by experts. Since MUC-3, BBN has been steadily increasing the proportion of the information extraction process that is statistically trained. Already in MET-1, our name-finding results were the output of a fully statistical, HMM-based model, and that statistical Identifinder^TM model was als...}, added-at = {2009-01-22T05:56:16.000+0100}, author = {Miller, Scott and Crystal, Michael and Fox, Heidi and Ramshaw, Lance and Schwartz, Richard and Stone, Rebecca and Weischedel, Ralph and Group, The Annotation}, bibsource = {OAI-PMH server at cs1.ist.psu.edu}, biburl = {https://www.bibsonomy.org/bibtex/2d1ec7b7fd6c83dd795c10c9efce9ec6b/kabloom}, booktitle = {MUC-7}, description = {literature-log.bib 1/21/2009}, interhash = {997ecd797997a6f2ffbdbf0c88aa3d6e}, intrahash = {d1ec7b7fd6c83dd795c10c9efce9ec6b}, keywords = {imported}, language = {en}, oai = {oai:CiteSeerPSU:241567}, rights = {unrestricted}, timestamp = {2009-01-22T05:56:16.000+0100}, title = {Algorithms That Learn To Extract Information {BBN}: Description Of The {S}ift System As Used For {MUC}-7}, url = {http://acl.ldc.upenn.edu/muc7/M98-0009.pdf}, year = 1998 }