1 Overview

AFNER is a named entity recogniser that uses a maximum entropy classifier in combination with pattern (regular expression) and list matching. It can be run independently or incorporated into another program.

The classifier used is adapted from YASMET by Franz Josef Och, and YASMET is used to generate the model file from training data. Consequently, training data is produced in the format required by YASMET.

AFNER provides flexibility to enable/disable features, and incorporate custom regular expressions and lists without any modification to the code. As such, the best features for a particular task can be chosen.

Features used in the classifier include token specific features (e.g. capitalisation), as well as complete or partial matches against items in a list or with a regular expression.