@hackage morfette0.3

=INTRODUCTION=

Morfette is a tool for supervised learning of inflectional morphology. Given a corpus of sentences annotated with lemmas and morphological labels, and optionally a lexicon, morfette learns how to morphologically analyse new sentences.

In the learning stage Morfette fits two separate logistic regression models: one for morphological tagging and one for lemmatization. The predictions of the models are combined dynamically and produce a globally plausible sequence of morphological-tag - lemma pairs for a sentence.

In Morfette lemmatization is cast as a classification task where a a lemmatization class corresponds to the specification of the edit operations which are needed to transform the inflected word form into the corresponding lemma.

The basic approach is described in (Chrupala et al 2008 and Chrupala 2008). The current version of Morfette uses an averaged perceptron to fit the models, rather than Maximum Entropy training. The lemmatization classes are Edit-Tree-based as described in (Chrupala 2008).

=LICENSE= The source code in the src directory is licensed under the BSD license.

=INSTALLATION= Pre-built binaries are available from the project website. If they don't work on your system you will need to build from source, using the GHC Haskell compiler. Build instructions are in [INSTALL]

=USAGE= Usage: morfette command [OPTION...] [ARG...] train: train models train [OPTION...] TRAIN-FILE MODEL-DIR --dict-file=PATH path to optional dictionary --language-configuration=es|pl|tr|.. language configuration --class-entropy-prune-threshold=NUM class prune threshold

predict: predict postags and lemmas using saved model data predict [OPTION...] MODEL-DIR --beam=+INT beam size to use --tokenize tokenize input

eval: evaluate morpho-tagging and lemmatization results eval [OPTION...] TRAIN-FILE GOLD-FILE TEST-FILE --ignore-case ignore case for evaluation --baseline-file=PATH path to baseline results --dict-file=PATH path to optional dictionary --ignore-punctuation ignore punctuation for evaluation --ignore-pos=POS-prefix ignore POS starting with POS-prefix for evaluation

=EXAMPLE USAGE= To train a new model: morfette train --dict-file=DICT TRAINING-FILE MODEL-DIR

To use the model in MODEL-DIR to analyze new data: morfette predict MODEL-DIR < TEST-DATA > ANALYZED-TEST-DATA

=DATA FORMAT= Morfette expects both training and testing data to be tokenized and split into sentences. The format of training data look like this:

Gómez Gómez np0000p sostiene sostener vmip3s0 que que cs la el da0fs0 propuesta propuesta ncfs000 no no rn cambiará cambiar vmif3s0 . . Fp

La el da0fs0 propuesta propuesta ncfs000 será ser vsif3s0 la el da0fs0 misma mismo pi0fs000

There is one token per line, with three columns separated by spaces or tabs. The columns contain word form, lemma and morphological tag respectively. Sentences are separated by an empty line. Text should be encoded in UTF-8.

Test data format is similar, except only the first column is needed:

Gómez sostiene que la propuesta no cambiará .

La propuesta será la misma

=References= [1] Grzegorz Chrupala, Georgiana Dinu and Josef van Genabith. 2008. Learning Morphology with Morfette. In Proceedings of LREC 2008. http://www.lrec-conf.org/proceedings/lrec2008/pdf/594_paper.pdf

[2] Grzegorz Chrupala. 2008. Towards a Machine-Learning Architecture for Lexical Functional Grammar Parsing. Chapter 6. PhD dissertation, Dublin City University. http://www.lsv.uni-saarland.de/personalPages/gchrupala/papers/phd.pdf