#2428Experiments evaluating the effectiveness of our answer resolution algorithm show a 35.0% relative improvement over our baseline system in the number of questions correctly answered, and a 32.8%improvement according to the average precision metric.
measure(ment),13-4-N03-1004,ak
resolution algorithm
</term>
show a 35.0 %
<term>
relative improvement
</term>
over our
<term>
baseline system
</term>
#2410Experiments evaluating the effectiveness of our answer resolution algorithm show a 35.0%relative improvement over our baseline system in the number of questions correctly answered, and a 32.8% improvement according to the average precision metric.
tech,37-1-N03-1004,ak
combining the results from different
<term>
answering agents
</term>
searching for
<term>
answers
</term>
#2344Motivated by the success of ensemble methods in machine learning and other areas of natural language processing, we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from differentanswering agents searching for answers in multiple corpora.
other,41-1-N03-1004,ak
answering agents
</term>
searching for
<term>
answers
</term>
in multiple
<term>
corpora
</term>
.
#2348Motivated by the success of ensemble methods in machine learning and other areas of natural language processing, we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching foranswers in multiple corpora.
tech,8-1-N03-1004,ak
of
<term>
ensemble methods
</term>
in
<term>
machine learning
</term>
and other areas of
<term>
natural language
#2315Motivated by the success of ensemble methods inmachine learning and other areas of natural language processing, we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching for answers in multiple corpora.
lr,44-1-N03-1004,ak
for
<term>
answers
</term>
in multiple
<term>
corpora
</term>
. The
<term>
answering agents
</term>
#2351Motivated by the success of ensemble methods in machine learning and other areas of natural language processing, we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching for answers in multiplecorpora.
tech,14-1-N03-1004,ak
learning
</term>
and other areas of
<term>
natural language processing
</term>
, we developed a multi-strategy and
#2321Motivated by the success of ensemble methods in machine learning and other areas ofnatural language processing, we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching for answers in multiple corpora.
other,23-4-N03-1004,ak
baseline system
</term>
in the number of
<term>
questions
</term>
correctly answered , and a 32.8 %
#2420Experiments evaluating the effectiveness of our answer resolution algorithm show a 35.0% relative improvement over our baseline system in the number ofquestions correctly answered, and a 32.8% improvement according to the average precision metric.
tech,5-1-N03-1004,ak
</term>
. Motivated by the success of
<term>
ensemble methods
</term>
in
<term>
machine learning
</term>
and
#2312Motivated by the success ofensemble methods in machine learning and other areas of natural language processing, we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching for answers in multiple corpora.
tech,6-4-N03-1004,ak
evaluating the effectiveness of our
<term>
answer resolution algorithm
</term>
show a 35.0 %
<term>
relative improvement
#2403Experiments evaluating the effectiveness of ouranswer resolution algorithm show a 35.0% relative improvement over our baseline system in the number of questions correctly answered, and a 32.8% improvement according to the average precision metric.
other,17-4-N03-1004,ak
relative improvement
</term>
over our
<term>
baseline system
</term>
in the number of
<term>
questions
</term>
#2414Experiments evaluating the effectiveness of our answer resolution algorithm show a 35.0% relative improvement over ourbaseline system in the number of questions correctly answered, and a 32.8% improvement according to the average precision metric.
tech,3-3-N03-1004,ak
statistical techniques . We present our
<term>
multi-level answer resolution algorithm
</term>
that combines results from the
<term>
#2376We present ourmulti-level answer resolution algorithm that combines results from the answering agents at the question, passage, and/or answer levels.
tech,1-2-N03-1004,ak
multiple
<term>
corpora
</term>
. The
<term>
answering agents
</term>
adopt fundamentally different strategies
#2354Theanswering agents adopt fundamentally different strategies, one utilizing primarily knowledge-based mechanisms and the other adopting statistical techniques.
other,16-3-N03-1004,ak
<term>
answering agents
</term>
at the
<term>
question , passage , and/or answer levels
</term>
. Experiments evaluating the effectiveness
#2389We present our multi-level answer resolution algorithm that combines results from the answering agents at thequestion , passage , and/or answer levels.
tech,12-3-N03-1004,ak
</term>
that combines results from the
<term>
answering agents
</term>
at the
<term>
question , passage ,
#2385We present our multi-level answer resolution algorithm that combines results from theanswering agents at the question, passage, and/or answer levels.
measure(ment),35-4-N03-1004,ak
improvement
</term>
according to the
<term>
average precision metric
</term>
. In this paper we present
<term>
ONTOSCORE
#2432Experiments evaluating the effectiveness of our answer resolution algorithm show a 35.0% relative improvement over our baseline system in the number of questions correctly answered, and a 32.8% improvement according to theaverage precision metric.
tech,26-1-N03-1004,ak
multi-strategy and multi-source approach to
<term>
question answering
</term>
which is based on combining the results
#2333Motivated by the success of ensemble methods in machine learning and other areas of natural language processing, we developed a multi-strategy and multi-source approach toquestion answering which is based on combining the results from different answering agents searching for answers in multiple corpora.