Content and Context: Two-Pronged Bootstrapped Learning for Regex-Formatted Entity Extraction

Abstract

Regular expressions are an important building block of rule-based information extraction systems. Regexes can encode rules to recognize instances of simple entities which can then feed into the identification of more complex cross-entity relationships. Manually crafting a regex that recognizes all possible instances of an entity is difficult since an entity can manifest in a variety of different forms. Thus, the problem of automatically generalizing manually crafted seed regexes to improve the recall of IE systems has attracted research attention. In this paper, we propose a bootstrapped approach to improve the recall for extraction of regex-formatted entities, with the only source of supervision being the seed regex. Our approach starts from a manually authored high precision seed regex for the entity of interest, and uses the matches of the seed regex and the context around these matches to identify more instances of the entity. These are then used to identify a set of diverse, high recall regexes that are representative of this entity. Through an empirical evaluation over multiple real world document corpora, we illustrate the effectiveness of our approach.

Learning to extract geographic information from internet router hostnames

“…Current research in entity extraction using regexes within the machine learning community relies on a human in the loop (e.g., [4,5,27,37,47,56]). Nevertheless, a fully automated holistic approach that first determines francetelecom refers to the operator of the router could allow an automated geolocation method to not consider that portion of a hostname as a possible geohint.…”