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evaluation is within an unsupervised
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lexical expansion
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scenario applied to a text categorization
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N09-2009 |
obtaining a special variant of
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lexical expansion
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. 1 Introduction Topical Text
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P09-2018 |
one of its major applications is
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lexical expansion
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, which is generally asymmetric
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P13-2051 |
words . Our evaluation shows that
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lexical expansion
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significantly improves performance
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P09-1051 |
dure . Thus , some of our valid
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lexical expansions
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might retrieve non-annotated
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P09-2018 |
annotated in the corpus . For our
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lexical expansion
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evaluation we considered the
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D15-1103 |
entity-specific context , we make use of
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lexical expansions
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, which have been successfully
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P09-2018 |
preliminary data analysis for
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lexical expansion
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. Finally , we note that in related
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P09-2018 |
at least twice . As a typical
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lexical expansion
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task we used the ACE 2005 events
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C96-1081 |
Section 3 explains the use of
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lexical expansion
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rules , whereas some concluding
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P13-2051 |
step scheme . First , we learn
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lexical expansion
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sets for argument words , such
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P09-1051 |
constructed baselines in a couple of
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lexical expansion
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and matching tasks . Our rule-base
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P09-1051 |
recall , indicating the need for
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lexical expansion
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. The second baseline is our
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H89-2008 |
development of a new module for
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lexical expansion
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via phonological rules , and
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D11-1140 |
online , repeatedly performing both
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lexical expansion
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( Step 1 ) and a parameter update
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J12-4006 |
nonterminal ) rules in ( 2a -- c ) and
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lexical expansions
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in ( 2d -- f ) . Annotated lexical
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D15-1103 |
entity-specific context ( using
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lexical expansions
|
) . We take all words in the
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P13-2051 |
languages perfectly . <title> Using
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Lexical Expansion
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to Learn Inference Rules from
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P09-2018 |
directional similarity measures for
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lexical expansion
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, and potentially for other tasks
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P13-2051 |
application task show that our
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lexical expansion
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approach significantly improves
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