Learning to Tag Multilingual Texts Through Observation
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TL;DR
This paper describes RoboTag, an advanced prototype for a machine learningbased multilingual information extraction system, and describes a general client/server architecture used in learning from observation and presents experimental results which compare RoboTag to both human-tagged keys and to the best hand-coded rule systems.
Abstract
This paper describes RoboTag, an advanced prototype for a machine learningbased multilingual information extraction system. First, we describe a general client/server architecture used in learning from observation. Then we give a detailed description of our novel decision-tree tagging approach. RoboTag performance for the proper noun tagging task in English and Japanese is compared against humantagged keys and to the best hand-coded pattern performance (as reported in the MUC and MET evaluation results). Related work and future directions are presented. 1