Learning to extract information from large domain-specific websites using sequential models

ABSTRACT

In this article we describe a novel information extraction task on the web and show how it can be solved effectively using the emerging conditional exponential models. The task involves learning to find specific goal pages on large domain-specific websites. An example of such a task is to find computer science publications starting from university root pages. We encode this as a sequential labeling problem solved using Conditional Random Fields (CRFs). These models enable us to exploit a wide variety of features including keywords and patterns extracted from and around hyperlinks and HTML pages, dependency among labels of adjacent pages, and existing databases of named entities in a unified probabilistic framework. This is an important advantage over previous rule-based or generative models for tackling the challenges of diversity on web data.

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