图 书 馆
基于强制定义因子图的实体分解和分割的双向联合推理Bi-Directional Joint Inference for Entity Resolution and Segmentation Using Imperatively-Defined Factor Graphs |
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| 课程网址: | http://videolectures.net/ecmlpkdd09_singh_bdjiersuidfg/ |
| 主讲教师: | Sameer Singh |
| 开课单位: | 马萨诸塞大学 |
| 开课时间: | 2009-10-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | There has been growing interest in using joint inference across multiple subtasks as a mechanism for avoiding the cascading accumulation of errors in traditional pipelines. Several recent papers demonstrate joint inference between the segmentation of entity mentions and their de-duplication, however, they have various weaknesses: inference information flows only in one direction, the number of uncertain hypotheses is severely limited, or the subtasks are only loosely coupled. This paper presents a highly-coupled, bi-directional approach to joint inference based on efficient Markov chain Monte Carlo sampling in a relational conditional random field. The model is specified with our new probabilistic programming language that leverages imperative constructs to define factor graph structure and operation. Experimental results show that our approach provides a dramatic reduction in error while also running faster than the previous state-of-the-art system. |
| 关 键 词: | 联合推理; 级联累积; 双向联合推理方法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-07-13：yumf |
| 阅读次数: | 47 |