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 semistructured 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 threelevel bootstrapping framework for the detection of semi-structured entities. We...

• Type: Conference
• Year: 2010
• Where: WWW
• Authors: Utku Irmak, Reiner Kraft
• Keywords: Internet Technology, Semi-structured Entities, Semistructured Named Entities, Time Entities, WWW 2010