Hierarchical Wrapper Induction for Semistructured Information Sources

By Ion Muslea, Steven Minton, Craig A. Knoblock. Autonomous Agents and Multi-Agent Systems, 2001

414 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Unsupervised named-entity extraction from the Web: An experimental study (2005, background): “Wrapper induction algorithms [24, 30] attempt to learn wrappers that exploit the structure of HTML to extract information from Web sites.”
Semi-supervised multi-task learning of structured prediction models for web information extraction (2011, methodology): “For example, the wrapper induction [19, 27] method requires supervision; it utilizes labeled data to learn extraction rules on the HTML tag structure of pages.”
ViDE: A Vision-Based Approach for Deep Web Data Extraction (2010, background): “The former, such as WIEN [15], Soft- Mealy [12], and Stalker [22], represents documents as sequences of tokens or characters, and generates delimiterbased extraction rules through a set of training examples.”
Zero-shot Entity Extraction from Web Pages (2014, background): “However, wrapper induction usually focuses on a small set of web domains, where the web pages in each domain follow a ﬁxed template (Muslea et al., 2001; Crescenzi et al., 2001; Cohen et al., 2002; Arasu and Garcia-Molina, 2003).”