measure(ment),23-4-N03-1004,bq |
baseline system
</term>
in the number of
<term>
|
questions correctly answered
|
</term>
, and a 32.8 % improvement according
|
#2419
Experiments 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,14-1-N03-1004,bq |
learning
</term>
and other areas of
<term>
|
natural language processing
|
</term>
, we developed a
<term>
multi-strategy
|
#2320
Motivated 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,16-3-N03-1004,bq |
<term>
answering agents
</term>
at the
<term>
|
question , passage , and/or answer levels
|
</term>
. Experiments evaluating the effectiveness
|
#2388
We present our multi-level answer resolution algorithm that combines results from the answering agents at thequestion , passage , and/or answer levels. |
measure(ment),35-4-N03-1004,bq |
32.8 % improvement according to the
<term>
|
average precision metric
|
</term>
. In this paper we present
<term>
ONTOSCORE
|
#2431
Experiments 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. |
lr,44-1-N03-1004,bq |
for
<term>
answers
</term>
in multiple
<term>
|
corpora
|
</term>
. The
<term>
answering agents
</term>
|
#2350
Motivated 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,17-2-N03-1004,bq |
mechanisms
</term>
and the other adopting
<term>
|
statistical techniques
|
</term>
. We present our
<term>
multi-level
|
#2369
The answering agents adopt fundamentally different strategies, one utilizing primarily knowledge-based mechanisms and the other adoptingstatistical techniques. |
tech,1-2-N03-1004,bq |
multiple
<term>
corpora
</term>
. The
<term>
|
answering agents
|
</term>
adopt fundamentally different strategies
|
#2353
Theanswering agents adopt fundamentally different strategies, one utilizing primarily knowledge-based mechanisms and the other adopting statistical techniques. |
tech,8-1-N03-1004,bq |
of
<term>
ensemble methods
</term>
in
<term>
|
machine learning
|
</term>
and other areas of
<term>
natural language
|
#2314
Motivated 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. |
tech,11-2-N03-1004,bq |
strategies , one utilizing primarily
<term>
|
knowledge-based mechanisms
|
</term>
and the other adopting
<term>
statistical
|
#2363
The answering agents adopt fundamentally different strategies, one utilizing primarilyknowledge-based mechanisms and the other adopting statistical techniques. |
tech,12-3-N03-1004,bq |
</term>
that combines results from the
<term>
|
answering agents
|
</term>
at the
<term>
question , passage ,
|
#2384
We present our multi-level answer resolution algorithm that combines results from theanswering agents at the question, passage, and/or answer levels. |
tech,5-1-N03-1004,bq |
</term>
. Motivated by the success of
<term>
|
ensemble methods
|
</term>
in
<term>
machine learning
</term>
and
|
#2311
Motivated 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. |
other,41-1-N03-1004,bq |
answering agents
</term>
searching for
<term>
|
answers
|
</term>
in multiple
<term>
corpora
</term>
.
|
#2347
Motivated 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. |
other,17-4-N03-1004,bq |
35.0 % relative improvement over our
<term>
|
baseline system
|
</term>
in the number of
<term>
questions correctly
|
#2413
Experiments 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,37-1-N03-1004,bq |
combining the results from different
<term>
|
answering agents
|
</term>
searching for
<term>
answers
</term>
|
#2343
Motivated 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. |
tech,6-4-N03-1004,bq |
evaluating the effectiveness of our
<term>
|
answer resolution algorithm
|
</term>
show a 35.0 % relative improvement
|
#2402
Experiments 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. |
tech,3-3-N03-1004,bq |
techniques
</term>
. We present our
<term>
|
multi-level answer resolution algorithm
|
</term>
that combines results from the
<term>
|
#2375
We present ourmulti-level answer resolution algorithm that combines results from the answering agents at the question, passage, and/or answer levels. |
tech,21-1-N03-1004,bq |
processing
</term>
, we developed a
<term>
|
multi-strategy and multi-source approach to question answering
|
</term>
which is based on combining the results
|
#2327
Motivated by the success of ensemble methods in machine learning and other areas of natural language processing, we developed amulti-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. |