document: A scalable machine-learning approach for semi-structured named entity recognition Named entity recognition studies the problem of locating and classifying parts of free text into a set of predefined categories . Although extensive research has focused on the detection of person , location and organization entities , there are many other entities of interest , including phone numbers , dates , times and currencies ( to name a few examples ) . We refer to these types of entities as `` semi-structured named entities '' , since they usually follow certain syntactic formats according to some conventions , although their structure is typically not well-defined . Regular expression solutions require significant amount of manual effort and supervised machine learning approaches rely on large sets of labeled training data . Therefore , these approaches do not scale when we need to support many semi-structured entity types in many languages and regions . In this paper , we study this problem and propose a novel three-level bootstrapping framework for the detection of semi-structured entities . We describe the proposed techniques for phone , date and time entities , and perform extensive evaluations on English , German , Polish , Swedish and Turkish documents . Despite the minimal input from the user , our approach can achieve 95 % precision and 84 % recall for phone entities , and 94 % precision and 81 % recall for date and time entities , on average . We also discuss implementation details and report run time performance results , which show significant improvements over regular expression based solutions .
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