tech,4-1-P03-1058,bq of interest . A central problem of <term> word sense disambiguation ( WSD ) </term> is the lack of <term> manually sense-tagged
lr,14-1-P03-1058,bq disambiguation ( WSD ) </term> is the lack of <term> manually sense-tagged data </term> required for <term> supervised learning
tech,19-1-P03-1058,bq sense-tagged data </term> required for <term> supervised learning </term> . In this paper , we evaluate an
lr,11-2-P03-1058,bq approach to automatically acquire <term> sense-tagged training data </term> from <term> English-Chinese parallel
lr,15-2-P03-1058,bq sense-tagged training data </term> from <term> English-Chinese parallel corpora </term> , which are then used for disambiguating
other,26-2-P03-1058,bq are then used for disambiguating the <term> nouns </term> in the <term> SENSEVAL-2 English lexical
other,29-2-P03-1058,bq disambiguating the <term> nouns </term> in the <term> SENSEVAL-2 English lexical sample task </term> . Our investigation reveals that
tech,5-3-P03-1058,bq Our investigation reveals that this <term> method of acquiring sense-tagged data </term> is promising . On a subset of the
other,7-4-P03-1058,bq On a subset of the most difficult <term> SENSEVAL-2 nouns </term> , the <term> accuracy </term> difference
measure(ment),11-4-P03-1058,bq <term> SENSEVAL-2 nouns </term> , the <term> accuracy </term> difference between the two approaches
lr,37-4-P03-1058,bq if we disregard the advantage that <term> manually sense-tagged data </term> have in their <term> sense coverage
other,43-4-P03-1058,bq sense-tagged data </term> have in their <term> sense coverage </term> . Our analysis also highlights the
other,10-5-P03-1058,bq highlights the importance of the issue of <term> domain dependence </term> in evaluating <term> WSD programs </term>
tech,14-5-P03-1058,bq domain dependence </term> in evaluating <term> WSD programs </term> . We describe the ongoing construction
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