An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Topic detection and tracking evaluation overview
Topic detection and tracking
Named entity extraction from noisy input: speech and OCR
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Improving information extraction by modeling errors in speech recognizer output
HLT '01 Proceedings of the first international conference on Human language technology research
Generalized algorithms for constructing statistical language models
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Using N-best lists for named entity recognition from Chinese speech
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Effects of word confusion networks on voice search
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Reshaping automatic speech transcripts for robust high-level spoken document analysis
AND '10 Proceedings of the fourth workshop on Analytics for noisy unstructured text data
Sibyl, a factoid question-answering system for spoken documents
ACM Transactions on Information Systems (TOIS)
Coupling knowledge-based and data-driven systems for named entity recognition
HYBRID '12 Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data
International Journal of Mobile Human Computer Interaction
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Traditional approaches to Information Extraction (IE) from speech input simply consist in applying text based methods to the output of an Automatic Speech Recognition (ASR) system. If it gives satisfaction with low Word Error Rate (WER) transcripts, we believe that a tighter integration of the IE and ASR modules can increase the IE performance in more difficult conditions. More specifically this paper focuses on the robust extraction of Named Entities from speech input where a temporal mismatch between training and test corpora occurs. We describe a Named Entity Recognition (NER) system, developed within the French Rich Broadcast News Transcription program ESTER, which is specifically optimized to process ASR transcripts and can be integrated into the search process of the ASR modules. Finally we show how some metadata information can be collected in order to adapt NER and ASR models to new conditions and how they can be used in a task of Named Entity indexation of spoken archives.