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different tasks , including noun
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. The work of Pitler et al. (
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recording underspecification of noun
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, for instance . is combined
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statistics to improve the noun
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. Volk ( 2001 ) exploited the
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2005 ) , who build models of noun
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. The initial-entry cost for
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the model and its impact on noun
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might direct us to better choices
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noun-verb PP attachments and noun
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. In this work , we first present
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analysis problem is that of noun
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, which is generally viewed as
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dependency relationships rather than
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disam - biguation . Several previous
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. Our research presents a noun
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algorithm which goes beyond the
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ambiguity problems such as noun
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, prepositional phrase ( PP )
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approaches . 2 Related work Noun
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has not received as much attention
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the n-gram : Application to Noun
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</title> Preslav Marti Abstract
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of exactly three nouns ( noun
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) . This is a simplification
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data ( Lin et al. , 2010 ) for
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disambiguation . Our research
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ordering , spelling correction , noun
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and verb part-of-speech disambiguation
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examples . <title> Multiword noun
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using Wikipedia </title> Caroline
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followed by two analysis tasks : noun
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( Sec - tion 5 ) and verb part-of-speech
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3.2.1 PMI Previous work on noun
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has used adjacency model ( Resnik
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applied to the problem of noun
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, produces higher accuracy than
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an unsupervised method for noun
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which extracts statistics from
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