@conference {13849,
	title = {Reranking for Sentence Boundary Detection in Conversational Speech},
	booktitle = {2006 IEEE International Conference on Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings},
	volume = {1},
	year = {2006},
	month = {2006/05/14/19},
	pages = {I-I - I-I},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present a reranking approach to sentence-like unit (SU) boundary detection, one of the EARS metadata extraction tasks. Techniques for generating relatively small n-best lists with high oracle accuracy are presented. For each candidate, features are derived from a range of information sources, including the output of a number of parsers. Our approach yields significant improvements over the best performing system from the NIST RT-04F community evaluation},
	keywords = {Automatic speech recognition, conversational speech, data mining, Ear, EARS metadata extraction tasks, Feature extraction, hidden Markov models, meta data, Model driven engineering, NIST, NIST RT-04F community evaluation, oracle accuracy, performance evaluation, reranking, sentence-like unit boundary detection, Speech processing, Speech recognition, Telephony},
	isbn = {1-4244-0469-X},
	doi = {10.1109/ICASSP.2006.1660078},
	author = {Roark,B. and Liu,Yang and Harper,M. and Stewart,R. and Lease,M. and Snover,M. and Shafran,I. and Dorr, Bonnie J and Hale,J. and Krasnyanskaya,A. and Yung,L.}
}
@conference {15305,
	title = {The role of knowledge in conceptual retrieval: a study in the domain of clinical medicine},
	booktitle = {Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval},
	series = {SIGIR {\textquoteright}06},
	year = {2006},
	month = {2006///},
	pages = {99 - 106},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Despite its intuitive appeal, the hypothesis that retrieval at the level of "concepts" should outperform purely term-based approaches remains unverified empirically. In addition, the use of "knowledge" has not consistently resulted in performance gains. After identifying possible reasons for previous negative results, we present a novel framework for "conceptual retrieval" that articulates the types of knowledge that are important for information seeking. We instantiate this general framework in the domain of clinical medicine based on the principles of evidence-based medicine (EBM). Experiments show that an EBM-based scoring algorithm dramatically outperforms a state-of-the-art baseline that employs only term statistics. Ablation studies further yield a better understanding of the performance contributions of different components. Finally, we discuss how other domains can benefit from knowledge-based approaches.},
	keywords = {Question answering, reranking, semantic models},
	isbn = {1-59593-369-7},
	doi = {10.1145/1148170.1148191},
	url = {http://doi.acm.org/10.1145/1148170.1148191},
	author = {Jimmy Lin and Demner-Fushman,Dina}
}
