Alearbres1 is a collection of scripts that is intended to facilitate the generation of random dependency trees.
You can used Alearbres to generate artificial treebanks with various constraints :
- random structures
- random (projective and non-projective) linearisations
- random linearisations that optimize for Dependency Length Minimization
Main script : random_structure.py
# create a random tree with 5 nodes
>>> t = DependencyTree(5)
Tree
Edges: [(1, 0), (2, 1), (3, 0), (4, 2)]# transform it to conllu format
>>> tree = t.toTree()
# write it to a file
conll3.trees2conllFile([tree], "sample_random-trees.conllu")You can visualize the tree as a stemma using the draw_forest() function from the script2 visualize_dependencies_udpipe.py
import visualize_dependencies_udpipe as visualize
visualize.draw_forest("sample_random-forest.conllu") Now if we want to build several trees at the same time, for example 10 trees of size 3 and 20 trees of size 20 :
specs = {3:10, 4:20}
forest = build_random_forest(specs)
conll3.trees2conllFile(forest, "sample_random-forest.conllu")Main script : random_linearisation.py
>>> conll = """1 le le _ _ _ 3 det _ _
2 petit petit _ _ _ 3 amod _ _
3 chat chat _ _ _ 4 subj _ _
4 dort dort _ _ _ 0 root _ _
5 très très _ _ _ 6 advmod _ _
6 bien bien _ _ _ 4 advmod _ _
"""
>>> tree = conll3.conll2tree(conll) # transform the conll string into a tree format
# create random linearisations
>>> random_tree = create_random_pj_linearisation(tree) # projective
r_nonproj = >>> create_random_nonpj_linearisation(tree) # non-projective
# look at the projective tree
>>> tree
1 dort dort _ _ _ 0 root _ _
2 le le _ _ _ 4 det _ _
3 petit petit _ _ _ 4 amod _ _
4 chat chat _ _ _ 1 subj _ _
5 très très _ _ _ 6 advmod _ _
6 bien bien _ _ _ 1 advmod _ _Which once translated into graphical format gives :
# write the trees to a conll file
trees = [random_tree, r_nonproj]
conll3.trees2conllFile(trees, "sample_randomly-linearised-trees.conllu") Main script : dlm.py
conll = """1 le le _ _ _ 3 det _ _
2 petit petit _ _ _ 3 amod _ _
3 chat chat _ _ _ 4 subj _ _
4 dort dort _ _ _ 0 root _ _
5 très très _ _ _ 6 advmod _ _
6 bien bien _ _ _ 4 advmod _ _
"""
# store the dependency tree in a dict
>>> tree = conll3.conll2tree(conll)
# find the optimal sequence of nodes
>>> linearization = optimal_linearization(tree)
>>> print(linearization)
[1,3,2,4,6,5]
# rewrite the conll according to the new linearisation
>>> new_tree = random_linearisation.rewrite_tree(tree, linearization)
# see the conll
>>> print(new_tree.conllu())
1 le le _ _ _ 2 det _ _
2 chat chat _ _ _ 4 subj _ _
3 petit petit _ _ _ 2 amod _ _
4 dort dort _ _ _ 0 root _ _
5 bien bien _ _ _ 4 advmod _ _
6 très très _ _ _ 5 advmod _ _Which gives us the nice tree below :
You can also of course use it on random trees to generate trees such as this one:
Great ! We're always interested in expanding the types of constraints that can be added during the generation process. If there is some specific constraint that you would be interested in, please submit a feature-request and we will look into it.
git clone https://github.com/marinecourtin/Alearbres.git
18/09/2019 : first release
1 : Alearbres is a mashup of aléatoire and arbres (random trees in French)
2 : This script was kindly provided by Peter Uhrig.
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