other,8-1-P04-2005,bq approach for automatically acquiring <term> English topic signatures </term> . Given a particular <term> concept
other,3-2-P04-2005,bq signatures </term> . Given a particular <term> concept </term> , or <term> word sense </term> , a <term>
other,6-2-P04-2005,bq particular <term> concept </term> , or <term> word sense </term> , a <term> topic signature </term> is
other,10-2-P04-2005,bq </term> , or <term> word sense </term> , a <term> topic signature </term> is a set of <term> words </term> that
other,16-2-P04-2005,bq <term> topic signature </term> is a set of <term> words </term> that tend to co-occur with it . <term>
other,0-3-P04-2005,bq </term> that tend to co-occur with it . <term> Topic signatures </term> can be useful in a number of <term>
tech,9-3-P04-2005,bq </term> can be useful in a number of <term> Natural Language Processing ( NLP ) </term> applications , such as <term> Word
tech,19-3-P04-2005,bq NLP ) </term> applications , such as <term> Word Sense Disambiguation ( WSD ) </term> and <term> Text Summarisation </term>
tech,26-3-P04-2005,bq Sense Disambiguation ( WSD ) </term> and <term> Text Summarisation </term> . Our method takes advantage of the
other,10-4-P04-2005,bq advantage of the different way in which <term> word senses </term> are lexicalised in <term> English </term>
other,15-4-P04-2005,bq word senses </term> are lexicalised in <term> English </term> and <term> Chinese </term> , and also
other,17-4-P04-2005,bq lexicalised in <term> English </term> and <term> Chinese </term> , and also exploits the large amount
other,26-4-P04-2005,bq also exploits the large amount of <term> Chinese text </term> available in <term> corpora </term> and
lr,30-4-P04-2005,bq <term> Chinese text </term> available in <term> corpora </term> and on the <term> Web </term> . We evaluated
lr,34-4-P04-2005,bq available in <term> corpora </term> and on the <term> Web </term> . We evaluated the <term> topic signatures
other,3-5-P04-2005,bq <term> Web </term> . We evaluated the <term> topic signatures </term> on a <term> WSD </term> task , where
tech,7-5-P04-2005,bq the <term> topic signatures </term> on a <term> WSD </term> task , where we trained a <term> second-order
tech,14-5-P04-2005,bq WSD </term> task , where we trained a <term> second-order vector co-occurrence algorithm </term> on standard <term> WSD datasets </term>
lr,20-5-P04-2005,bq co-occurrence algorithm </term> on standard <term> WSD datasets </term> , with promising results . This paper
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