Multi-modal Information Extraction from Text, Semi-structured, and Tabular Data on the Web

被引：10
|
作者：
Dong, Xin Luna [1 ]
Hajishirzi, Hannaneh [2 ]
Lockard, Colin [3 ]
Shiralkar, Prashant [1 ]
机构：
[1] Amazon, Seattle, WA 98109 USA
[2] Univ Washington, Allen Inst AI, Seattle, WA USA
[3] Univ Washington, Amazon, Seattle, WA USA
来源：
KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING | 2020年
关键词：
Information extraction; Web extraction; Semi-structured data; Web mining;
D O I：
10.1145/3394486.3406468
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
How do we surface the large amount of information present in HTML documents on the Web, from news articles to Rotten Tomatoes pages to tables of sports scores? Such information can enable a variety of applications including knowledge base construction, question answering, recommendation, and more. In this tutorial, we present approaches for information extraction (IE) from Web data that can be differentiated along two key dimensions: 1) the diversity in data modality that is leveraged, e.g. text, visual, XML/HTML, and 2) the thrust to develop scalable approaches with zero to limited human supervision.
引用
收藏
页码：3543 / 3544
页数：2
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