Crescenzi et al, 2001
Citation
Carlson, A., S. Schafer. 2008. Bootstrapping Information Extraction from Semi-structured Web Pages. ECML PKDD '08: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I, 2008, 195-210, Berlin, Heidelberg.
Online version
Summary
This paper introduces a novel technique for automatic wrapper generation by comparing HTML pages and building a wrapper based on the similarity between web pages. This technique can be applied on websites that contain large amount of data (i.e. data-intensive). They also have assumed that the webpages of the given website have fairly similar structure. The main advantages of this technique are:
- This technique does not require any interaction with user during the process of wrapper generation. This extends the applicability of their technique to automatically learn wrappers for input website without getting any supervision from human.
- The technique doesn't have any prior knowledge about the structure of the input web pages.
The intuition behind their technique is to use global features to infer rules about the local features. For example suppose that we know the name of a set of books. Then by looking at webpages of Amazon.com and by searching the name of books we can infer that the position and font of the book title
is the same in most the webpages. We can then use these two features (position and font of book title in web pages) to extract new book titles.
They have described both generative and discriminative approaches for classification and extraction tasks. Global features are govern by parameters that are shared by all data and local features are shared only by a subset of data. For example in information extraction task, all the words in a webpage (without considering formatting) can be considered as global features. On the other hand, features such as position of a text box or color of a text are local features.
They have tested their method on two different datasets. The first dataset contains 1000 HTML documents. Each document is automatically divided into a set of words with similar layout characteristics and then are hand-labeled as containing or not containing a job title. The local and global features for this domain are the same as what we discussed above. The second dataset contain 42,548 web pages from 330 web sites which each web page is hand-labeled as if it is a press release or not press release. The global feature is a set of word in each webpage and local feature is the URL of the webpage. Their experimental result have shown that this approach can obtain high precision and low/moderate recall.
