图 书 馆
LODifier:从非结构化文本生成链接数据LODifier: Generating Linked Data from Unstructured Text |
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| 课程网址: | http://videolectures.net/eswc2012_augenstein_lodifier/ |
| 主讲教师: | Isabelle Augenstein |
| 开课单位: | 谢菲尔德大学 |
| 开课时间: | 2010-07-04 |
| 课程语种: | 英语 |
| 中文简介: |  从文本中自动提取信息并将其转换为形式描述是语义Web研究和计算语言学的重要目标。提取的信息可用于各种任务，例如本体生成，问题回答和信息检索。 LODifier是一种将深度语义分析与命名实体识别，词义歧义消除和受控语义网络词汇相结合的方法，以便从文本中提取命名实体及其之间的关系，并将其转换为与DBpedia和WordNet链接的RDF表示形式。我们介绍了我们工具的架构，并讨论了设计决策。对该工具的评估清楚地证明了其在诸如信息提取和计算文档相似性等任务中的潜力。 |
| 课程简介: | The automated extraction of information from text and its transformation into a formal description is an important goal of in both Semantic Web research and computational linguistics. The extracted information can be used for a variety of tasks such as ontology generation, question answering and information retrieval. LODifier is an approach that combines deep semantic analysis with named entity recognition, word-sense disambiguation and controlled Semantic Web vocabularies in order to extract named entities and relations between them from text and to convert them into an RDF representation which is linked to DBpedia and WordNet. We present the architecture of our tool and discuss design decisions made. Evaluations of the tool give clear evidence of its potential for tasks like information extraction and computing document similarity. |
| 关 键 词: | 信息提取; 语义Web; 文本信息自动提取; 计算语言学; 计算文档相似性 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-05-26：zyk |
| 最后编审: | 2021-05-26：zyk |
| 阅读次数: | 43 |