| tech,1-2-N03-1004,ak | multiple <term> corpora </term> . The <term> | answering agents | </term> adopt fundamentally different strategies | #2354 The answering 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 the answering agents at the question, passage, and/or answer levels. | |
| 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 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 multiple corpora. | |
| 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 different answering agents searching for answers in multiple corpora. |
