图 书 馆
海报：利用先验领域知识构建基于HMM的语义标记器，对完全未注释的数据进行训练Poster: Using Prior Domain Knowledge to Build HMM-Based Semantic Tagger Trained on Completely Unannotated Data |
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| 课程网址: | http://videolectures.net/icml08_mengistu_upd/ |
| 主讲教师: | Kinfe Tadesse Mengistu |
| 开课单位: | 马格德堡大学 |
| 开课时间: | 2008-08-11 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this paper, we propose a robust statistical semantic tagging model trained on completely unannotated data. The approach relies mainly on prior domain knowledge to counterbalance the lack of semantically annotated treebank data. The proposed method encodes longer contextual information by grouping strongly related semantic concepts together into cohesive units. The method is based on hidden Markov model (HMM) and offers high ambiguity resolution power, outputs semantically rich information, and requires relatively low human effort. The approach yields high-performance models that are evaluated on two different corpora in two application domains in English and German. |
| 关 键 词: | 统计语义标记; 树库数据; 隐马尔可夫模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-19：lxf |
| 阅读次数: | 69 |