图 书 馆
从命名实体认知和链接（NEEL）中学到的经验教训Lessons Learnt from the Named Entity rEcognition and Linking (NEEL) |
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| 课程网址: | http://videolectures.net/iswc2018_van_erp_lessons_neel/ |
| 主讲教师: | Marieke van Erp |
| 开课单位: | 阿姆斯特丹Vrije大学（VU）理学院 |
| 开课时间: | 2018-11-22 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The large number of tweets generated daily is providing decision makers with means to obtain insights into recent events around the globe in near real-time. The main barrier for extracting such insights is the impossibility of manual inspection of a diverse and dynamic amount of information. This problem has attracted the attention of industry and research communities,resulting in algorithms for the automatic extraction of semantics in tweets and linking them to machine readable resources. While a tweet is shallowly comparable to any other textual content, it hides a complex and challenging structure that requires domain-specific computational approaches for mining semantics from it. The NEEL challenge series, established in 2013, has contributed to the collection of emerging trends in the field and definition of standardised benchmark corpora for entity recognition and linking in tweets, ensuring high quality labelled data that facilitates comparisons between different approaches. This article reports the findings and lessons learnt through an analysis of specific characteristics of the created corpora, limitations, lessons learnt from the different participants and pointers for furthering the field of entity recognition and linking in tweets. |
| 关 键 词: | 提取此类见解; 检查各种动态信息量; 挖掘语义; 推进实体识别 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-01-16：cyh |
| 最后编审: | 2023-01-16：cyh |
| 阅读次数: | 21 |