@article{
 title = {Information extraction from broadcast news},
 type = {article},
 year = {2000},
 keywords = {information extraction,language modelling,named entity},
 pages = {12},
 volume = {358},
 websites = {http://hdl.handle.net/1842/976},
 publisher = {The Royal Society},
 id = {5c075247-b885-3364-9753-13ab2acdad3c},
 created = {2012-02-09T21:39:35.000Z},
 file_attached = {true},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Gotoh2000},
 private_publication = {false},
 abstract = {This paper discusses the development of trainable statistical models for extracting content from television and radio news broadcasts. In particular, we concentrate on statistical finite-state models for identifying proper names and other named entities in broadcast speech. Two models are presented: the first represents name class information as a word attribute; the second represents both word-word and class-class transitions explicitly. A common n-gram-based formulation is used for both models. The task of named-entity identification is characterized by relatively sparse training data, and issues related to smoothing are discussed. Experiments are reported using the DARPA/NIST Hub-4E evaluation for North American broadcast news.},
 bibtype = {article},
 author = {Gotoh, Yoshihiko and Renals, Steve},
 journal = {Phil Trans R Soc Lond A},
 number = {1769}
}

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