Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Information-extraction (IE) research typically focuses on clean-text inputs. Hover, an IE engine serving real applications yields many false alarms due to less-ll-formed input. For example, IE in a multilingual broadcast processing system has to deal with inaccurate automatic transcription and translation. The resulting presence of non-target-language text in this case, and non-language material interspersed in data from other applications, raise the research problem of making IE robust to such noisy input text.  address one such IE task: entity-mention detection.  describe augmenting a statistical mention-detection system in order to reduce false alarms from spurious passages. The diverse nature of input noise leads us to pursue a multi-faceted approach to robustness. For our English-language system, at various miss rates  eliminate 97% of false alarms on inputs from other Latin-alphabet languages. In another experiment, representing scenarios in which genre-specific training is infeasible,  process real financial-transactions text containing mixed languages and data-set codes. On these data, because  do not train on data like it,  achieve a smaller but significant improvement. These gains come with virtually no loss in accuracy on clean English text.
['Radu Florian', 'John F. Pitrelli', 'Salim Roukos', 'Imed Zitouni']
I am writing a research paper on a idea Improving Mention Detection Robustness to Noisy Input
[{"from": "human", "value": "I am writing a research paper on a idea Improving Mention Detection Robustness to Noisy Input"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Information-extraction (IE) research typically focuses on clean-text inputs. Hover, an IE engine serving real applications yields many false alarms due to less-ll-formed input. For example, IE in a multilingual broadcast processing system has to deal with inaccurate automatic transcription and translation. The resulting presence of non-target-language text in this case, and non-language material interspersed in data from other applications, raise the research problem of making IE robust to such noisy input text.  address one such IE task: entity-mention detection.  describe augmenting a statistical mention-detection system in order to reduce false alarms from spurious passages. The diverse nature of input noise leads us to pursue a multi-faceted approach to robustness. For our English-language system, at various miss rates  eliminate 97% of false alarms on inputs from other Latin-alphabet languages. In another experiment, representing scenarios in which genre-specific training is infeasible,  process real financial-transactions text containing mixed languages and data-set codes. On these data, because  do not train on data like it,  achieve a smaller but significant improvement. These gains come with virtually no loss in accuracy on clean English text."}]