</term>
between
<term>
entities
</term>
is challenging
#9266Extractingsemantic relationships between entities is challenging.
other,4-1-P05-1053,ak
semantic relationships
</term>
between
<term>
entities
</term>
is challenging . This paper investigates
#9269Extracting semantic relationships betweenentities is challenging.
other,7-2-P05-1053,ak
investigates the incorporation of diverse
<term>
lexical , syntactic and semantic knowledge
</term>
in
<term>
feature-based relation extraction
#9280This paper investigates the incorporation of diverselexical , syntactic and semantic knowledge in feature-based relation extraction using SVM.
tech,14-2-P05-1053,ak
syntactic and semantic knowledge
</term>
in
<term>
feature-based relation extraction
</term>
using
<term>
SVM
</term>
. Our study
#9287This paper investigates the incorporation of diverse lexical, syntactic and semantic knowledge infeature-based relation extraction using SVM.
tech,18-2-P05-1053,ak
feature-based relation extraction
</term>
using
<term>
SVM
</term>
. Our study illustrates that the
#9291This paper investigates the incorporation of diverse lexical, syntactic and semantic knowledge in feature-based relation extraction usingSVM.
other,6-3-P05-1053,ak
Our study illustrates that the base
<term>
phrase chunking information
</term>
is very effective for
<term>
relation
#9299Our study illustrates that the basephrase chunking information is very effective for relation extraction and contributes to most of the performance improvement from syntactic aspect while additional information from full parsing gives limited further enhancement.
tech,13-3-P05-1053,ak
information
</term>
is very effective for
<term>
relation extraction
</term>
and contributes to most of the performance
#9306Our study illustrates that the base phrase chunking information is very effective forrelation extraction and contributes to most of the performance improvement from syntactic aspect while additional information from full parsing gives limited further enhancement.
tech,30-3-P05-1053,ak
while additional information from
<term>
full parsing
</term>
gives limited further enhancement
#9323Our study illustrates that the base phrase chunking information is very effective for relation extraction and contributes to most of the performance improvement from syntactic aspect while additional information fromfull parsing gives limited further enhancement.
model,8-4-P05-1053,ak
that most of useful information in
<term>
full parse trees
</term>
for
<term>
relation extraction
</term>
#9338This suggests that most of useful information infull parse trees for relation extraction is shallow and can be captured by chunking.
tech,12-4-P05-1053,ak
in
<term>
full parse trees
</term>
for
<term>
relation extraction
</term>
is shallow and can be captured by
#9342This suggests that most of useful information in full parse trees forrelation extraction is shallow and can be captured by chunking.
tech,21-4-P05-1053,ak
</term>
is shallow and can be captured by
<term>
chunking
</term>
. We also demonstrate how
<term>
semantic
#9351This suggests that most of useful information in full parse trees for relation extraction is shallow and can be captured bychunking.
other,4-5-P05-1053,ak
chunking
</term>
. We also demonstrate how
<term>
semantic information
</term>
such as
<term>
WordNet
</term>
and
<term>
#9357We also demonstrate howsemantic information such as WordNet and Name List, can be used in feature-based relation extraction to further improve the performance.
lr-prod,8-5-P05-1053,ak
semantic information
</term>
such as
<term>
WordNet
</term>
and
<term>
Name List
</term>
, can be
#9361We also demonstrate how semantic information such asWordNet and Name List, can be used in feature-based relation extraction to further improve the performance.
tech,17-5-P05-1053,ak
<term>
Name List
</term>
, can be used in
<term>
feature-based relation extraction
</term>
to further improve the performance
#9370We also demonstrate how semantic information such as WordNet and Name List, can be used infeature-based relation extraction to further improve the performance.
lr-prod,3-6-P05-1053,ak
the performance . Evaluation on the
<term>
ACE corpus
</term>
shows that effective incorporation
#9382Evaluation on theACE corpus shows that effective incorporation of diverse features enables our system outperform previously best-reported systems on the 24 ACE relation subtypes and significantly outperforms tree kernel-based systems by over 20 in F-measure on the 5 ACE relation types.
other,11-6-P05-1053,ak
effective incorporation of diverse
<term>
features
</term>
enables our system outperform previously
#9390Evaluation on the ACE corpus shows that effective incorporation of diversefeatures enables our system outperform previously best-reported systems on the 24 ACE relation subtypes and significantly outperforms tree kernel-based systems by over 20 in F-measure on the 5 ACE relation types.
other,22-6-P05-1053,ak
previously best-reported systems on the 24
<term>
ACE relation subtypes
</term>
and significantly outperforms
<term>
#9401Evaluation on the ACE corpus shows that effective incorporation of diverse features enables our system outperform previously best-reported systems on the 24ACE relation subtypes and significantly outperforms tree kernel-based systems by over 20 in F-measure on the 5 ACE relation types.
tech,28-6-P05-1053,ak
</term>
and significantly outperforms
<term>
tree kernel-based systems
</term>
by over 20 in
<term>
F-measure
</term>
#9407Evaluation on the ACE corpus shows that effective incorporation of diverse features enables our system outperform previously best-reported systems on the 24 ACE relation subtypes and significantly outperformstree kernel-based systems by over 20 in F-measure on the 5 ACE relation types.
measure(ment),35-6-P05-1053,ak
kernel-based systems
</term>
by over 20 in
<term>
F-measure
</term>
on the 5
<term>
ACE relation types
</term>
#9414Evaluation on the ACE corpus shows that effective incorporation of diverse features enables our system outperform previously best-reported systems on the 24 ACE relation subtypes and significantly outperforms tree kernel-based systems by over 20 inF-measure on the 5 ACE relation types.
other,39-6-P05-1053,ak
20 in
<term>
F-measure
</term>
on the 5
<term>
ACE relation types
</term>
.
<term>
Sentence boundary detection
#9418Evaluation on the ACE corpus shows that effective incorporation of diverse features enables our system outperform previously best-reported systems on the 24 ACE relation subtypes and significantly outperforms tree kernel-based systems by over 20 in F-measure on the 5ACE relation types.