NB!

This is Språkbanken's inofficial version of the paradigmextract library. The main version can be found here.

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Paradigm learning and paradigm prediction

The software collection in this repository is related to a body of scientific work on paradigm learning and paradigm prediction, of which the following publication is the latest one. See the reference list for previous work.

[Forsberg, M; Hulden, M. (2016). Learning Transducer Models for Morphological Analysis from Example Inflections. In Proceedings of StatFSM. Association for Computational Linguistics.] (http://anthology.aclweb.org/W16-2405)

Quick reference

Paradigm learning: pextract.py

Description

Extract paradigmatic representations from input inflection tables. See Section 2 in Forsberg and Hulden (2016) for details.

Example

$ python src/pextract.py < data/es_verb_train.txt > es_verb.p

Non-probabilistic morphological analyzers: morphanalyzer.py

Description

Create a foma-compatible morphological analyzer from a paradigm file. The analyzer is non-probabilistic.

Options:

• -o recreate original data (all vars must be exactly instantiated as seen in training data)
• -c constrain variables by generalizing (default pvalue = 0.05)
• -u unconstrained (all variables are defined as ?+)
• -p use together with -c
• -s keep different analyzers separate instead of merging with priority union (may be necessary for some analyzers)
• -n name of binary foma file to compile to

Any combination of the above may be used. The analyzers are combined by priority union, e.g. -o -c -u would yield an analyzer [ Goriginal .P. Gconstrained .P. Gunconstrained ].

Example

$ python src/morphanalyzer.py -o -c es_verb.p > es_verb.foma

Probabilistic morphological analyzers: morphparser.py

Description

Create a probabilistic morphological analyzer from a paradigm file.

Reads one or more whitespace-separated words from STDIN and returns the most plausible analysis for the set in the format: SCORE NAME_OF_PARADIGM VARIABLES WORDFORM1:BASEFORM,MSD#WORDFORM2:BASEFORM,MSD...

Flags:

• -k num print the k best analyses
• -t print the entire table for the best analysis
• -d print debug info
• -n num use an nth order ngram model for selecting best paradigm (an n-gram model for variables in the paradigm is used)

Example

$ echo "coger cojo" | python morphparser.py ./../paradigms/spanish_verbs.p -k 1 -t