On the learnability and usage of acyclic probabilistic finite automata
Journal of Computer and System Sciences - Special issue on the eighth annual workshop on computational learning theory, July 5–8, 1995
Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Named entity extraction from noisy input: speech and OCR
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
A maximum entropy model for prepositional phrase attachment
HLT '94 Proceedings of the workshop on Human Language Technology
Automatic summarization of voicemail messages using lexical and prosodic features
ACM Transactions on Speech and Language Processing (TSLP)
Information extraction from voicemail transcripts
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Formatting time-aligned ASR transcripts for readability
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Domain adaptation of rule-based annotators for named-entity recognition tasks
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
International Journal of Mobile Human Computer Interaction
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In this paper we address the problem of extracting key pieces of information from voicemail messages, such as the identity and phone number of the caller. This task differs from the named entity task in that the information we are interested in is a subset of the named entities in the message, and consequently, the need to pick the correct subset makes the problem more difficult. Also, the caller's identity may include information that is not typically associated with a named entity. In this work, we present three information extraction methods, one based on hand-crafted rules, one based on maximum entropy tagging, and one based on probabilistic transducer induction. We evaluate their performance on both manually transcribed messages and on the output of a speech recognition system.