acopost Ingo Schroder is a speech in 2002 at the University of Hamburg, Germany marked the completion of the toolkit. The main achievement of tagging algorithms, as well as uation and fusion algorithm based on examples, maximum entropy, 2 yuan hidden horse, based on the conversion rules, four kinds of speech and so on. Language is used in perl and c, the code is relatively short, very suitable for learning.

acopost_note
............\acopost-1.8.6.tar.gz
............\comment
............\.......\array.c
............\.......\cooked2lex.pl
............\.......\cooked2ngram.pl
............\.......\cooked2wtree.pl
............\.......\et.c
............\.......\gis.c
............\.......\gis.h
............\.......\met.c
............\.......\t3.c
............\.......\tbt.c
............\.......\util.c
............\data
............\....\cooked.txt
............\....\dict
............\....\et
............\....\..\known.etf
............\....\..\known.wtree
............\....\..\res.et
............\....\..\unknown.etf
............\....\..\unknown.wtree
............\....\met
............\....\...\model
............\....\...\res1
............\....\...\res2
............\....\pk9801.txt
............\....\raw.txt
............\....\t3
............\....\..\ngram.t3
............\....\..\res.t3
............\....\..\trans_pb.t3
............\....\tbt
............\....\...\cooked_lex.txt
............\....\...\cooked_rul.txt
............\....\...\dict.tbt
............\....\...\res.tbt
............\....\...\rules.tbt
............\....\...\transitions.tbt.templates
............\....\...\unknown.tbt.templates
............\....\test
............\et.txt
............\met.txt
............\note_acopost.txt
............\README.txt
............\references
............\..........\(BR94)A rule-based approach to prepositional phrase attachment disambiguation.pdf
............\..........\(Bran00)TnT- a statistical part-of-speech tagger.pdf
............\..........\(Bril92)A simple rule-based part of speech tagger.pdf
............\..........\(Bril93')Transformation-based error-driven parsing.pdf
............\..........\(Bril93)Automatic grammar induction and parsing free text - a transformation-based approach.pdf
............\..........\(Bril94)Some advances in transformation-based part of speech tagging.pdf
............\..........\(Bril95')Unsupervised learning lf disambiguation rules for part of speech tagging.pdf
............\..........\(Bril95)Transformation-based error-driven learning and natural language processing - a case study in part-of-speech tagging.pdf
............\..........\(DBW97)IGTree using trees for compression and classification in lazy learning algorithms.pdf
............\..........\(DZBG96)MBT A memory-based part of speech tagger generator.pdf
............\..........\(For05)The Viterbi Algorithm- A Personal History.pdf
............\..........\(NF01)Transformation-based learning in the fast lane.pdf
............\..........\(Rat96)A maximum entropy model for part-of-speech tagging.pdf
............\..........\(Rat98)Maximum entropy models for natural language ambiguity resolution.pdf
............\..........\(Scha03)0410.pdf
............\..........\(Schr02')A case study in part-of-speech tagging using the ICOPOST toolkit.pdf
............\..........\(Schr02)ug.pdf
............\..........\(Xue03)Chinese word segmentation as character tagging.pdf
............\..........\(ZD99)Recent advances in memory-based part-of-speech tagging.pdf
............\t3.txt
............\tbt.txt
............\tool
............\....\pk2raw.py
............\....\raw2cooked.py
............\....\tbt_split_corpus.py

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