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,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,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. |
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. |
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. |
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. |
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. |
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,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. |
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,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,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,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. |
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. |
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. |
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. |