ACOPOST - A COllection of POS Taggers

ACOPOST is a set of freely available POS taggers modeled after well-known techniques. The programs are written in C (aiming for extreme portability and code correctness/safety) and run under various UNIX flavors (and probably even under Windows). ACOPOST currently consists of four taggers which are based on different frameworks:

1. Maximum Entropy Tagger MET: This tagger uses an iterative procedure to successively improve parameters for a set of features that help to distinguish between relevant contexts.
2. Trigram Tagger T3: This kind of tagger is based on Hidden Markov Models (HMM) where the states are tag pairs that emit words, i. e., it's based on transitional and lexical probabilities.
3. Error-driven Transformation-based Tagger TBT: Transformation rules are learned from an annotated corpus which change the currently assigned tag depending on triggering context conditions.
4. Example-based tagger ET: Example-based models (also called memory-based, instance-based or distance-based) rest on the assumption that cognitive behavior can be achieved by looking at past experiences that resemble the current problem rather than learning and applying abstract rules.