图 书 馆
用计数量词丰富知识库Enriching Knowledge Bases with Counting Quantifiers |
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| 课程网址: | http://videolectures.net/iswc2018_mirza_enriching_knowledge_bases... |
| 主讲教师: | Paramita Mirza |
| 开课单位: | 马克斯·普朗克信息研究所 |
| 开课时间: | 2018-10-22 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Information extraction traditionally focuses on extracting relations between identifiable entities, such as . Yet, texts often also contain Counting information, stating that a subject is in a specific relation with a number of objects, without mentioning the objects themselves, for example, "California is divided into 58 counties". Such counting quantifiers can help in a variety of tasks such as query answering or knowledge base curation, but are neglected by prior work. This paper develops the first full-fledged system for extracting counting information from text, called CINEX. We employ distant supervision using fact counts from a knowledge base as training seeds, and develop novel techniques for dealing with several challenges: (i) non-maximal training seeds due to the incompleteness of knowledge bases, (ii) sparse and skewed observations in text sources, and (iii) high diversity of linguistic patterns. Experiments with five human-evaluated relations show that CINEX can achieve 60% average precision for extracting counting information. In a large-scale experiment, we demonstrate the potential for knowledge base enrichment by applying CINEX to 2,474 frequent relations in Wikidata. CINEX can assert the existence of 2.5M facts for 110 distinct relations, which is 28% more than the existing Wikidata facts for these relations. |
| 关 键 词: | 查询解答或知识库管理; 提取计数信息的系统; 语言模式的高度多样性; 现有的Wikidata事实 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-12-30：cyh |
| 最后编审: | 2023-05-15：cyh |
| 阅读次数: | 34 |