Thus , the second class of SBD systems employs machine learning techniques such as decision tree classifiers ( Riley 1989 ) , neural networks ( Palmer and Hearst 1994 ) , and maximum-entropy modeling ( Reynar and Ratnaparkhi 1997 ) .
some applications of treebased modeling to speech and language indexing”
Michael D Riley
Automatically trainable software is generally seen as a way of producing systems that are quickly retrainable for a new corpus , for a new domain , or even for another language . Thus , the second class of SBD systems employs machine learning techniques such as decision tree classifiers ( Riley 1989 ) , neural networks ( Palmer and Hearst 1994 ) , and maximum-entropy modeling ( Reynar and Ratnaparkhi 1997 ) . Machine learning systems treat the SBD task as a classification problem , using features such as word spelling , capitalization , suffix , and word class found in the local context of a potential sentence-terminating punctuation sign .
Thus , the second class of SBD systems employs machine learning techniques such as decision tree classifiers ( @@CITATION ) , neural networks ( Palmer and Hearst 1994 ) , and maximum-entropy modeling ( Reynar and Ratnaparkhi 1997 ) .