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Query P03-1058 14 >
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tech,4-1-P03-1058,bq	A central problem of <term> word sense disambiguation ( WSD ) </term> is the lack of <term> manually sense-tagged data </term> required for <term> supervised learning </term> .	#4803 A central problem ofword sense disambiguation ( WSD ) is the lack of manually sense-tagged data required for supervised learning.
lr,14-1-P03-1058,bq	A central problem of <term> word sense disambiguation ( WSD ) </term> is the lack of <term> manually sense-tagged data </term> required for <term> supervised learning </term> .	#4813 A central problem of word sense disambiguation (WSD) is the lack ofmanually sense-tagged data required for supervised learning.
tech,19-1-P03-1058,bq	A central problem of <term> word sense disambiguation ( WSD ) </term> is the lack of <term> manually sense-tagged data </term> required for <term> supervised learning </term> .	#4818 A central problem of word sense disambiguation (WSD) is the lack of manually sense-tagged data required forsupervised learning.
lr,11-2-P03-1058,bq	In this paper , we evaluate an approach to automatically acquire <term> sense-tagged training data </term> from <term> English-Chinese parallel corpora </term> , which are then used for disambiguating the <term> nouns </term> in the <term> SENSEVAL-2 English lexical sample task </term> .	#4832 In this paper, we evaluate an approach to automatically acquiresense-tagged training data from English-Chinese parallel corpora, which are then used for disambiguating the nouns in the SENSEVAL-2 English lexical sample task.
lr,15-2-P03-1058,bq	In this paper , we evaluate an approach to automatically acquire <term> sense-tagged training data </term> from <term> English-Chinese parallel corpora </term> , which are then used for disambiguating the <term> nouns </term> in the <term> SENSEVAL-2 English lexical sample task </term> .	#4836 In this paper, we evaluate an approach to automatically acquire sense-tagged training data fromEnglish-Chinese parallel corpora, which are then used for disambiguating the nouns in the SENSEVAL-2 English lexical sample task.
other,26-2-P03-1058,bq	In this paper , we evaluate an approach to automatically acquire <term> sense-tagged training data </term> from <term> English-Chinese parallel corpora </term> , which are then used for disambiguating the <term> nouns </term> in the <term> SENSEVAL-2 English lexical sample task </term> .	#4847 In this paper, we evaluate an approach to automatically acquire sense-tagged training data from English-Chinese parallel corpora, which are then used for disambiguating thenouns in the SENSEVAL-2 English lexical sample task.
other,29-2-P03-1058,bq	In this paper , we evaluate an approach to automatically acquire <term> sense-tagged training data </term> from <term> English-Chinese parallel corpora </term> , which are then used for disambiguating the <term> nouns </term> in the <term> SENSEVAL-2 English lexical sample task </term> .	#4850 In this paper, we evaluate an approach to automatically acquire sense-tagged training data from English-Chinese parallel corpora, which are then used for disambiguating the nouns in theSENSEVAL-2 English lexical sample task.
tech,5-3-P03-1058,bq	Our investigation reveals that this <term> method of acquiring sense-tagged data </term> is promising .	#4861 Our investigation reveals that thismethod of acquiring sense-tagged data is promising.
other,7-4-P03-1058,bq	On a subset of the most difficult <term> SENSEVAL-2 nouns </term> , the <term> accuracy </term> difference between the two approaches is only 14.0 % , and the difference could narrow further to 6.5 % if we disregard the advantage that <term> manually sense-tagged data </term> have in their <term> sense coverage </term> .	#4876 On a subset of the most difficultSENSEVAL-2 nouns, the accuracy difference between the two approaches is only 14.0%, and the difference could narrow further to 6.5% if we disregard the advantage that manually sense-tagged data have in their sense coverage.
measure(ment),11-4-P03-1058,bq	On a subset of the most difficult <term> SENSEVAL-2 nouns </term> , the <term> accuracy </term> difference between the two approaches is only 14.0 % , and the difference could narrow further to 6.5 % if we disregard the advantage that <term> manually sense-tagged data </term> have in their <term> sense coverage </term> .	#4880 On a subset of the most difficult SENSEVAL-2 nouns, theaccuracy difference between the two approaches is only 14.0%, and the difference could narrow further to 6.5% if we disregard the advantage that manually sense-tagged data have in their sense coverage.
lr,37-4-P03-1058,bq	On a subset of the most difficult <term> SENSEVAL-2 nouns </term> , the <term> accuracy </term> difference between the two approaches is only 14.0 % , and the difference could narrow further to 6.5 % if we disregard the advantage that <term> manually sense-tagged data </term> have in their <term> sense coverage </term> .	#4906 On a subset of the most difficult SENSEVAL-2 nouns, the accuracy difference between the two approaches is only 14.0%, and the difference could narrow further to 6.5% if we disregard the advantage thatmanually sense-tagged data have in their sense coverage.
other,43-4-P03-1058,bq	On a subset of the most difficult <term> SENSEVAL-2 nouns </term> , the <term> accuracy </term> difference between the two approaches is only 14.0 % , and the difference could narrow further to 6.5 % if we disregard the advantage that <term> manually sense-tagged data </term> have in their <term> sense coverage </term> .	#4912 On a subset of the most difficult SENSEVAL-2 nouns, the accuracy difference between the two approaches is only 14.0%, and the difference could narrow further to 6.5% if we disregard the advantage that manually sense-tagged data have in theirsense coverage.
other,10-5-P03-1058,bq	Our analysis also highlights the importance of the issue of <term> domain dependence </term> in evaluating <term> WSD programs </term> .	#4925 Our analysis also highlights the importance of the issue ofdomain dependence in evaluating WSD programs.
tech,14-5-P03-1058,bq	Our analysis also highlights the importance of the issue of <term> domain dependence </term> in evaluating <term> WSD programs </term> .	#4929 Our analysis also highlights the importance of the issue of domain dependence in evaluatingWSD programs.


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