%0 Conference Paper %1 aggour2006 %A Aggour, Kareem S. %A Interrante, John %A Gokcen, Ibrahim %B Proceedings of the 2006 AAAI Workshop on EventExtractionand Synthesis %D 2006 %K dataset_own dependency_parse event_extraction %P 30--35 %T Integrating Techniques for Event-based Business Intelligence Gathering %U http://www.aaai.org/Papers/Workshops/2006/WS-06-07/WS06-07-007.pdf %X Gathering news events on companies to provide business intelligence to financial investors and creditors is a challenging problem. With a plethora of online news providers and tens of thousands of companies to monitor, automating the extraction and fusion of events is crucial. We developed an intelligent agent-based component framework to query and extract events from multiple providers. This framework integrates multiple machine learning and natural language processing techniques to down-select articles and extract targeted events. Results indicate that our system is capable of extracting focal events on a variety of topics with effective precision and recall.

@inproceedings{aggour2006, abstract = {Gathering news events on companies to provide business intelligence to financial investors and creditors is a challenging problem. With a plethora of online news providers and tens of thousands of companies to monitor, automating the extraction and fusion of events is crucial. We developed an intelligent agent-based component framework to query and extract events from multiple providers. This framework integrates multiple machine learning and natural language processing techniques to down-select articles and extract targeted events. Results indicate that our system is capable of extracting focal events on a variety of topics with effective precision and recall.}, added-at = {2009-10-22T15:19:46.000+0200}, author = {Aggour, Kareem S. and Interrante, John and Gokcen, Ibrahim}, biburl = {https://www.bibsonomy.org/bibtex/2e53c71c737d0a9eee1d8adf5c355d893/jnothman}, booktitle = {Proceedings of the 2006 AAAI Workshop on EventExtractionand Synthesis}, description = {System intended to aid business intelligence: detect fiscal health problems for companies. Eight event types; document-level classification; sentence-level extraction. Multiple news sources. Simple search query (with term expansion) for each event type. Documents then classified by Linear SVM (at 87% accuracy for some types). Can be incrementally trained. Find relevant sentences with manually-built patterns, and then matching stemmed keywords (nouns and verbs). Dependency graph-based rules match event participant roles.}, interhash = {1ce0b80d03b110a8a238ad4022b0332c}, intrahash = {e53c71c737d0a9eee1d8adf5c355d893}, keywords = {dataset_own dependency_parse event_extraction}, pages = {30--35}, timestamp = {2009-10-22T15:19:46.000+0200}, title = {Integrating Techniques for Event-based Business Intelligence Gathering}, url = {http://www.aaai.org/Papers/Workshops/2006/WS-06-07/WS06-07-007.pdf}, year = 2006 }