Boilerplate Detection and Recoding

Many information access applications have to tackle natural language texts that contain a large proportion of repeated and mostly invariable patterns --- called boilerplates ---, such as automatic templates, headers, signatures and table formats. These domain-specific standard formulations are usually much longer than traditional collocations or standard noun phrases and typically cover one or more sentences. Such motifs clearly have a non-compositional meaning and an ideal document representation should reflect this phenomenon. We propose here a method that detects automatically and in an unsupervised way such motifs; and enriches the document representation by including specific features for these motifs. We experimentally show that this document recoding strategy leads to improved classification on different collections.