Dependency Parsing¶
Note
This tutorial is available as an IPython notebook here.
%%time
import malaya
CPU times: user 4.53 s, sys: 1.05 s, total: 5.58 s
Wall time: 5.85 s
Describe supported dependencies¶
malaya.dependency.describe()
acl - clausal modifier of noun
advcl - adverbial clause modifier
advmod - adverbial modifier
amod - adjectival modifier
appos - appositional modifier
aux - auxiliary
case - case marking
ccomp - clausal complement
compound - compound
compound:plur - plural compound
conj - conjunct
cop - cop
csubj - clausal subject
dep - dependent
det - determiner
fixed - multi-word expression
flat - name
iobj - indirect object
mark - marker
nmod - nominal modifier
nsubj - nominal subject
obj - direct object
parataxis - parataxis
root - root
xcomp - open clausal complement
you can read more from https://universaldependencies.org/treebanks/id_pud/index.html
string = 'Dr Mahathir menasihati mereka supaya berhenti berehat dan tidur sebentar sekiranya mengantuk ketika memandu.'
List available transformer Dependency models¶
malaya.dependency.available_transformer()
{'bert': ['426.0 MB',
'arc accuracy: 0.855',
'types accuracy: 0.848',
'root accuracy: 0.920'],
'tiny-bert': ['59.5 MB',
'arc accuracy: 0.718',
'types accuracy: 0.694',
'root accuracy: 0.886'],
'albert': ['50.0 MB',
'arc accuracy: 0.811',
'types accuracy: 0.793',
'root accuracy: 0.879'],
'tiny-albert': ['24.8 MB',
'arc accuracy: 0.708',
'types accuracy: 0.673',
'root accuracy: 0.817'],
'xlnet': ['450.2 MB',
'arc accuracy: 0.931',
'types accuracy: 0.925',
'root accuracy: 0.947'],
'alxlnet': ['50.0 MB',
'arc accuracy: 0.894',
'types accuracy: 0.886',
'root accuracy: 0.942']}
Make sure you can check accuracy chart from here first before select a model, https://malaya.readthedocs.io/en/latest/Accuracy.html#dependency-parsing
The best model in term of accuracy is XLNET.
Load xlnet dependency model¶
model = malaya.dependency.transformer(model = 'xlnet')
WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/function/__init__.py:54: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.
WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/function/__init__.py:55: The name tf.GraphDef is deprecated. Please use tf.compat.v1.GraphDef instead.
WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/function/__init__.py:49: The name tf.InteractiveSession is deprecated. Please use tf.compat.v1.InteractiveSession instead.
d_object, tagging, indexing = model.predict(string)
d_object.to_graphvis()
Voting stack model¶
alxlnet = malaya.dependency.transformer(model = 'alxlnet')
tagging, indexing = malaya.stack.voting_stack([model, alxlnet, model], string)
malaya.dependency.dependency_graph(tagging, indexing).to_graphvis()
downloading frozen /Users/huseinzolkepli/Malaya/dependency/alxlnet/base model
51.0MB [00:50, 1.01MB/s]
Dependency graph object¶
To initiate a dependency graph from dependency models, you need to call
malaya.dependency.dependency_graph.
graph = malaya.dependency.dependency_graph(tagging, indexing)
graph
<malaya.function.parse_dependency.DependencyGraph at 0x164e67e90>
Get nodes¶
graph.nodes
defaultdict(<function malaya.function.parse_dependency.DependencyGraph.__init__.<locals>.<lambda>()>,
{0: {'address': 0,
'word': None,
'lemma': None,
'ctag': 'TOP',
'tag': 'TOP',
'feats': None,
'head': None,
'deps': defaultdict(list, {'root': [3]}),
'rel': None},
1: {'address': 1,
'word': 'Dr',
'lemma': '_',
'ctag': '_',
'tag': '_',
'feats': '_',
'head': 3,
'deps': defaultdict(list, {'flat': [2]}),
'rel': 'nsubj'},
3: {'address': 3,
'word': 'menasihati',
'lemma': '_',
'ctag': '_',
'tag': '_',
'feats': '_',
'head': 0,
'deps': defaultdict(list,
{'nsubj': [1], 'obj': [4], 'ccomp': [6]}),
'rel': 'root'},
2: {'address': 2,
'word': 'Mahathir',
'lemma': '_',
'ctag': '_',
'tag': '_',
'feats': '_',
'head': 1,
'deps': defaultdict(list, {}),
'rel': 'flat'},
4: {'address': 4,
'word': 'mereka',
'lemma': '_',
'ctag': '_',
'tag': '_',
'feats': '_',
'head': 3,
'deps': defaultdict(list, {}),
'rel': 'obj'},
5: {'address': 5,
'word': 'supaya',
'lemma': '_',
'ctag': '_',
'tag': '_',
'feats': '_',
'head': 6,
'deps': defaultdict(list, {}),
'rel': 'case'},
6: {'address': 6,
'word': 'berhenti',
'lemma': '_',
'ctag': '_',
'tag': '_',
'feats': '_',
'head': 3,
'deps': defaultdict(list,
{'case': [5], 'ccomp': [7], 'conj': [9]}),
'rel': 'ccomp'},
7: {'address': 7,
'word': 'berehat',
'lemma': '_',
'ctag': '_',
'tag': '_',
'feats': '_',
'head': 6,
'deps': defaultdict(list, {}),
'rel': 'ccomp'},
8: {'address': 8,
'word': 'dan',
'lemma': '_',
'ctag': '_',
'tag': '_',
'feats': '_',
'head': 9,
'deps': defaultdict(list, {}),
'rel': 'cc'},
9: {'address': 9,
'word': 'tidur',
'lemma': '_',
'ctag': '_',
'tag': '_',
'feats': '_',
'head': 6,
'deps': defaultdict(list,
{'cc': [8],
'advmod': [10],
'amod': [12],
'advcl': [14]}),
'rel': 'conj'},
10: {'address': 10,
'word': 'sebentar',
'lemma': '_',
'ctag': '_',
'tag': '_',
'feats': '_',
'head': 9,
'deps': defaultdict(list, {}),
'rel': 'advmod'},
11: {'address': 11,
'word': 'sekiranya',
'lemma': '_',
'ctag': '_',
'tag': '_',
'feats': '_',
'head': 12,
'deps': defaultdict(list, {}),
'rel': 'advmod'},
12: {'address': 12,
'word': 'mengantuk',
'lemma': '_',
'ctag': '_',
'tag': '_',
'feats': '_',
'head': 9,
'deps': defaultdict(list, {'advmod': [11]}),
'rel': 'amod'},
13: {'address': 13,
'word': 'ketika',
'lemma': '_',
'ctag': '_',
'tag': '_',
'feats': '_',
'head': 14,
'deps': defaultdict(list, {}),
'rel': 'case'},
14: {'address': 14,
'word': 'memandu.',
'lemma': '_',
'ctag': '_',
'tag': '_',
'feats': '_',
'head': 9,
'deps': defaultdict(list, {'case': [13]}),
'rel': 'advcl'}})
Flat the graph¶
list(graph.triples())
[(('menasihati', '_'), 'nsubj', ('Dr', '_')),
(('Dr', '_'), 'flat', ('Mahathir', '_')),
(('menasihati', '_'), 'obj', ('mereka', '_')),
(('menasihati', '_'), 'ccomp', ('berhenti', '_')),
(('berhenti', '_'), 'case', ('supaya', '_')),
(('berhenti', '_'), 'ccomp', ('berehat', '_')),
(('berhenti', '_'), 'conj', ('tidur', '_')),
(('tidur', '_'), 'cc', ('dan', '_')),
(('tidur', '_'), 'advmod', ('sebentar', '_')),
(('tidur', '_'), 'amod', ('mengantuk', '_')),
(('mengantuk', '_'), 'advmod', ('sekiranya', '_')),
(('tidur', '_'), 'advcl', ('memandu.', '_')),
(('memandu.', '_'), 'case', ('ketika', '_'))]
Generate networkx¶
Make sure you already installed networkx, pip install networkx
digraph = graph.to_networkx()
digraph
<networkx.classes.multidigraph.MultiDiGraph at 0x1a875a110>
import networkx as nx
import matplotlib.pyplot as plt
nx.draw_networkx(digraph)
plt.show()
<Figure size 640x480 with 1 Axes>
digraph.edges()
OutMultiEdgeDataView([(1, 3), (2, 1), (4, 3), (5, 6), (6, 3), (7, 6), (8, 9), (9, 6), (10, 9), (11, 12), (12, 9), (13, 14), (14, 9)])
digraph.nodes()
NodeView((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14))
labels = {i:graph.get_by_address(i)['word'] for i in digraph.nodes()}
labels
{1: 'Dr',
2: 'Mahathir',
3: 'menasihati',
4: 'mereka',
5: 'supaya',
6: 'berhenti',
7: 'berehat',
8: 'dan',
9: 'tidur',
10: 'sebentar',
11: 'sekiranya',
12: 'mengantuk',
13: 'ketika',
14: 'memandu.'}
plt.figure(figsize=(15,5))
nx.draw_networkx(digraph,labels=labels)
plt.show()
