Concordance

Query a 631 (29,418.6 per million)

<term> long distance constraints </term> in	a	<term> sentence </term> or <term> paragraph </term>	#21227 This paper introduces a simple mixture language model that attempts to capture long distance constraints in a sentence or paragraph.
</term> . The models were constructed using	a	5K <term> vocabulary </term> and trained using	#21247 The models were constructed using a 5K vocabulary and trained using a 76 million word Wall Street Journal text corpus.
<term> vocabulary </term> and trained using	a	76 million <term> word </term><term> Wall Street	#21253 The models were constructed using a 5K vocabulary and trained using a 76 million word Wall Street Journal text corpus.
recognition system </term> , experiments show	a	7 % improvement in <term> recognition accuracy	#21271 Using the BU recognition system, experiments show a 7% improvement in recognition accuracy with the mixture trigram models as compared to using a trigram model.
trigram models </term> as compared to using	a	<term> trigram model </term> . This paper describes	#21287 Using the BU recognition system, experiments show a 7% improvement in recognition accuracy with the mixture trigram models as compared to using a trigram model.
trigram model </term> . This paper describes	a	method of <term> detecting speechrepairs </term>	#21294 This paper describes a method of detecting speechrepairs that uses a part-of-speech tagger.
<term> detecting speechrepairs </term> that uses	a	<term> part-of-speech tagger </term> . The <term>	#21301 This paper describes a method of detecting speechrepairs that uses a part-of-speech tagger.
speechrepairs </term> , and so is able to mark	a	<term> transition </term> either as a <term>	#21322 The tagger is given knowledge about category transitions for speechrepairs, and so is able to mark a transition either as a likely repair or as fluent speech.
to mark a <term> transition </term> either as	a	<term> likely repair </term> or as <term> fluent	#21326 The tagger is given knowledge about category transitions for speechrepairs, and so is able to mark a transition either as a likely repair or as fluent speech.
understanding technology </term> is implemented in	a	system called <term> IDUS ( Intelligent Document	#21395 Our document understanding technology is implemented in a system called IDUS (Intelligent Document Understanding System), which creates the data for a text retrieval application and the automatic generation of hypertext links.
System ) </term> , which creates the data for	a	<term> text retrieval application </term> and	#21411 Our document understanding technology is implemented in a system called IDUS (Intelligent Document Understanding System), which creates the data for a text retrieval application and the automatic generation of hypertext links.


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