Concordance

Query P04-2005 19 (885.8 per million)

other,8-1-P04-2005,bq	We present a novel approach for automatically acquiring <term> English topic signatures </term> .	#6901 We present a novel approach for automatically acquiringEnglish topic signatures.
other,3-2-P04-2005,bq	Given a particular <term> concept </term> , or <term> word sense </term> , a <term> topic signature </term> is a set of <term> words </term> that tend to co-occur with it .	#6908 Given a particularconcept, or word sense, a topic signature is a set of words that tend to co-occur with it.
other,6-2-P04-2005,bq	Given a particular <term> concept </term> , or <term> word sense </term> , a <term> topic signature </term> is a set of <term> words </term> that tend to co-occur with it .	#6911 Given a particular concept, orword sense, a topic signature is a set of words that tend to co-occur with it.
other,10-2-P04-2005,bq	Given a particular <term> concept </term> , or <term> word sense </term> , a <term> topic signature </term> is a set of <term> words </term> that tend to co-occur with it .	#6915 Given a particular concept, or word sense, atopic signature is a set of words that tend to co-occur with it.
other,16-2-P04-2005,bq	Given a particular <term> concept </term> , or <term> word sense </term> , a <term> topic signature </term> is a set of <term> words </term> that tend to co-occur with it .	#6921 Given a particular concept, or word sense, a topic signature is a set ofwords that tend to co-occur with it.
other,0-3-P04-2005,bq	Given a particular <term> concept </term> , or <term> word sense </term> , a <term> topic signature </term> is a set of <term> words </term> that tend to co-occur with it . <term> Topic signatures </term> can be useful in a number of <term> Natural Language Processing ( NLP ) </term> applications , such as <term> Word Sense Disambiguation ( WSD ) </term> and <term> Text Summarisation </term> .	#6929 Given a particular concept, or word sense, a topic signature is a set of words that tend to co-occur with it.Topic signatures can be useful in a number of Natural Language Processing (NLP) applications, such as Word Sense Disambiguation (WSD) and Text Summarisation.
tech,9-3-P04-2005,bq	<term> Topic signatures </term> can be useful in a number of <term> Natural Language Processing ( NLP ) </term> applications , such as <term> Word Sense Disambiguation ( WSD ) </term> and <term> Text Summarisation </term> .	#6938 Topic signatures can be useful in a number ofNatural Language Processing ( NLP ) applications, such as Word Sense Disambiguation (WSD) and Text Summarisation.
tech,19-3-P04-2005,bq	<term> Topic signatures </term> can be useful in a number of <term> Natural Language Processing ( NLP ) </term> applications , such as <term> Word Sense Disambiguation ( WSD ) </term> and <term> Text Summarisation </term> .	#6948 Topic signatures can be useful in a number of Natural Language Processing (NLP) applications, such asWord Sense Disambiguation ( WSD ) and Text Summarisation.
tech,26-3-P04-2005,bq	<term> Topic signatures </term> can be useful in a number of <term> Natural Language Processing ( NLP ) </term> applications , such as <term> Word Sense Disambiguation ( WSD ) </term> and <term> Text Summarisation </term> .	#6955 Topic signatures can be useful in a number of Natural Language Processing (NLP) applications, such as Word Sense Disambiguation (WSD) andText Summarisation.
other,10-4-P04-2005,bq	Our method takes advantage of the different way in which <term> word senses </term> are lexicalised in <term> English </term> and <term> Chinese </term> , and also exploits the large amount of <term> Chinese text </term> available in <term> corpora </term> and on the <term> Web </term> .	#6968 Our method takes advantage of the different way in whichword senses are lexicalised in English and Chinese, and also exploits the large amount of Chinese text available in corpora and on the Web.
other,15-4-P04-2005,bq	Our method takes advantage of the different way in which <term> word senses </term> are lexicalised in <term> English </term> and <term> Chinese </term> , and also exploits the large amount of <term> Chinese text </term> available in <term> corpora </term> and on the <term> Web </term> .	#6973 Our method takes advantage of the different way in which word senses are lexicalised inEnglish and Chinese, and also exploits the large amount of Chinese text available in corpora and on the Web.
other,17-4-P04-2005,bq	Our method takes advantage of the different way in which <term> word senses </term> are lexicalised in <term> English </term> and <term> Chinese </term> , and also exploits the large amount of <term> Chinese text </term> available in <term> corpora </term> and on the <term> Web </term> .	#6975 Our method takes advantage of the different way in which word senses are lexicalised in English andChinese, and also exploits the large amount of Chinese text available in corpora and on the Web.
other,26-4-P04-2005,bq	Our method takes advantage of the different way in which <term> word senses </term> are lexicalised in <term> English </term> and <term> Chinese </term> , and also exploits the large amount of <term> Chinese text </term> available in <term> corpora </term> and on the <term> Web </term> .	#6984 Our method takes advantage of the different way in which word senses are lexicalised in English and Chinese, and also exploits the large amount ofChinese text available in corpora and on the Web.
lr,30-4-P04-2005,bq	Our method takes advantage of the different way in which <term> word senses </term> are lexicalised in <term> English </term> and <term> Chinese </term> , and also exploits the large amount of <term> Chinese text </term> available in <term> corpora </term> and on the <term> Web </term> .	#6988 Our method takes advantage of the different way in which word senses are lexicalised in English and Chinese, and also exploits the large amount of Chinese text available incorpora and on the Web.
lr,34-4-P04-2005,bq	Our method takes advantage of the different way in which <term> word senses </term> are lexicalised in <term> English </term> and <term> Chinese </term> , and also exploits the large amount of <term> Chinese text </term> available in <term> corpora </term> and on the <term> Web </term> .	#6992 Our method takes advantage of the different way in which word senses are lexicalised in English and Chinese, and also exploits the large amount of Chinese text available in corpora and on theWeb.
other,3-5-P04-2005,bq	We evaluated the <term> topic signatures </term> on a <term> WSD </term> task , where we trained a <term> second-order vector co-occurrence algorithm </term> on standard <term> WSD datasets </term> , with promising results .	#6997 We evaluated thetopic signatures on a WSD task, where we trained a second-order vector co-occurrence algorithm on standard WSD datasets, with promising results.
tech,7-5-P04-2005,bq	We evaluated the <term> topic signatures </term> on a <term> WSD </term> task , where we trained a <term> second-order vector co-occurrence algorithm </term> on standard <term> WSD datasets </term> , with promising results .	#7001 We evaluated the topic signatures on aWSD task, where we trained a second-order vector co-occurrence algorithm on standard WSD datasets, with promising results.
tech,14-5-P04-2005,bq	We evaluated the <term> topic signatures </term> on a <term> WSD </term> task , where we trained a <term> second-order vector co-occurrence algorithm </term> on standard <term> WSD datasets </term> , with promising results .	#7008 We evaluated the topic signatures on a WSD task, where we trained asecond-order vector co-occurrence algorithm on standard WSD datasets, with promising results.
lr,20-5-P04-2005,bq	We evaluated the <term> topic signatures </term> on a <term> WSD </term> task , where we trained a <term> second-order vector co-occurrence algorithm </term> on standard <term> WSD datasets </term> , with promising results .	#7014 We evaluated the topic signatures on a WSD task, where we trained a second-order vector co-occurrence algorithm on standardWSD datasets, with promising results.


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