Improving Mention Detection Robustness to Noisy Input

Information-extraction (IE) research typically focuses on clean-text inputs. However, an IE engine serving real applications yields many false alarms due to less-well-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. We address one such IE task: entity-mention detection. We 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 we eliminate 97% of false alarms on inputs from other Latin-alphabet languages. In another experiment, representing scenarios in which genre-specific traini...

• Type: Journal
• Year: 2010
• Where: EMNLP
• Authors: Radu Florian, John F. Pitrelli, Salim Roukos, Imed Zitouni
• Keywords: EMNLP 2010, False Alarms, Inaccurate Automatic Transcription, Natural Language Processing, Noisy Input Text