| lr,15-2-P03-1058,ak | 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> . | #4838 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. | |
| lr,37-4-P03-1058,ak | On a subset of the most difficult <term> SENSEVAL-2 nouns </term> , the <term> accuracy difference </term> 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 sense coverage . | #4908 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,10-5-P03-1058,ak | Our analysis also highlights the importance of the issue of <term> domain dependence </term> in evaluating <term> WSD programs </term> . | #4927 Our analysis also highlights the importance of the issue ofdomain dependence in evaluating WSD programs. | |
| tech,14-5-P03-1058,ak | Our analysis also highlights the importance of the issue of <term> domain dependence </term> in evaluating <term> WSD programs </term> . | #4931 Our analysis also highlights the importance of the issue of domain dependence in evaluatingWSD programs. | |
| tech,4-1-P03-1058,ak | 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> . | #4805 A central problem ofword sense disambiguation ( WSD ) is the lack of manually sense-tagged data required for supervised learning. | |
| other,26-2-P03-1058,ak | 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> . | #4849 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. | |
| tech,19-1-P03-1058,ak | 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> . | #4820 A central problem of word sense disambiguation (WSD) is the lack of manually sense-tagged data required forsupervised learning. | |
| lr,14-1-P03-1058,ak | 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> . | #4815 A central problem of word sense disambiguation (WSD) is the lack ofmanually sense-tagged data required for supervised learning. | |
| other,29-2-P03-1058,ak | 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> . | #4852 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. | |
| measure(ment),11-4-P03-1058,ak | On a subset of the most difficult <term> SENSEVAL-2 nouns </term> , the <term> accuracy difference </term> 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 sense coverage . | #4882 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,11-2-P03-1058,ak | 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> . | #4834 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,8-3-P03-1058,ak | Our investigation reveals that this method of acquiring <term> sense-tagged data </term> is promising . | #4866 Our investigation reveals that this method of acquiringsense-tagged data is promising. | |
| other,7-4-P03-1058,ak | On a subset of the most difficult <term> SENSEVAL-2 nouns </term> , the <term> accuracy difference </term> 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 sense coverage . | #4878 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. |
