| tech,6-4-N03-1004,ak | evaluating the effectiveness of our <term> | answer resolution algorithm | </term> show a 35.0 % <term> relative improvement | #2403 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,37-1-N03-1004,ak | combining the results from different <term> | answering agents | </term> searching for <term> answers </term> | #2344 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,1-2-N03-1004,ak | multiple <term> corpora </term> . The <term> | answering agents | </term> adopt fundamentally different strategies | #2354 Theanswering agents adopt fundamentally different strategies, one utilizing primarily knowledge-based mechanisms and the other adopting statistical techniques. | |
| tech,12-3-N03-1004,ak | </term> that combines results from the <term> | answering agents | </term> at the <term> question , passage , | #2385 We present our multi-level answer resolution algorithm that combines results from theanswering agents at the question, passage, and/or answer levels. | |
| other,41-1-N03-1004,ak | answering agents </term> searching for <term> | answers | </term> in multiple <term> corpora </term> . | #2348 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. | |
| measure(ment),35-4-N03-1004,ak | improvement </term> according to the <term> | average precision metric | </term> . In this paper we present <term> ONTOSCORE | #2432 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. | |
| other,17-4-N03-1004,ak | relative improvement </term> over our <term> | baseline system | </term> in the number of <term> questions </term> | #2414 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. | |
| lr,44-1-N03-1004,ak | for <term> answers </term> in multiple <term> | corpora | </term> . The <term> answering agents </term> | #2351 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,5-1-N03-1004,ak | </term> . Motivated by the success of <term> | ensemble methods | </term> in <term> machine learning </term> and | #2312 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. | |
| measure(ment),31-4-N03-1004,ak | </term> correctly answered , and a 32.8 % <term> | improvement | </term> according to the <term> average precision | #2428 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 the average precision metric. | |
| tech,8-1-N03-1004,ak | of <term> ensemble methods </term> in <term> | machine learning | </term> and other areas of <term> natural language | #2315 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,3-3-N03-1004,ak | statistical techniques . We present our <term> | multi-level answer resolution algorithm | </term> that combines results from the <term> | #2376 We present ourmulti-level answer resolution algorithm that combines results from the answering agents at the question, passage, and/or answer levels. | |
| tech,14-1-N03-1004,ak | learning </term> and other areas of <term> | natural language processing | </term> , we developed a multi-strategy and | #2321 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,ak | <term> answering agents </term> at the <term> | question , passage , and/or answer levels | </term> . Experiments evaluating the effectiveness | #2389 We present our multi-level answer resolution algorithm that combines results from the answering agents at thequestion , passage , and/or answer levels. | |
| tech,26-1-N03-1004,ak | multi-strategy and multi-source approach to <term> | question answering | </term> which is based on combining the results | #2333 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 toquestion 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 % | #2420 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. | |
| measure(ment),13-4-N03-1004,ak | resolution algorithm </term> show a 35.0 % <term> | relative improvement | </term> over our <term> baseline system </term> | #2410 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 the average precision metric. |
