def __init__(self, labeled):
self.labeled = labeled
self.numIterations = 5
self.posTypes = []
self.labelTypes = []
self.model = PerceptronModel(self.labeled)
def __init__(self, labeled):
self.labeled = labeled
self.numIterations = 5
self.posTypes = []
self.labelTypes = []
self.model = PerceptronModel(self.labeled)
class Parser:
def __init__(self, labeled):
self.labeled = labeled
self.numIterations = 5
self.posTypes = []
self.labelTypes = []
self.model = PerceptronModel(self.labeled)
# self.model = DeepLearningModel(self.labeled, self.posTypes, self.labelTypes)
# self.model = SvmModel(self.labeled)
def initialize(self, sentence):
# ID FORM LEMMA CPOSTAG POSTAG FEATS HEAD DEPREL PHEAD PDEPREL
self.root = [
'0', 'ROOT', 'ROOT', 'ROOT', 'ROOT', 'ROOT', '-1', 'ROOT', 'ROOT',
'ROOT'
]
self.buff = [self.root] + list(
reversed(sentence)) # buff = [ROOT, N, N-1, ... 1]
self.stack = list()
self.arcs = {} # {dependentId: headId}
self.labels = {} # {dependentId: dependentLabel}
self.wordsThatHaveHeads = {} # {wordId: word}
self.transitions = list()
self.dependentIDs = defaultdict(list) # {headId: [dependentId, ...]}
# Calculate the leftmost and rightmost children for each node in the sentence
# Note: At test time this data is not used.
for word in sentence:
self.dependentIDs[word[C.HEAD]].append(word[C.ID])
# Record types that were seen
def trackTypes(self, sentence):
for word in sentence:
if word[C.POSTAG] not in self.posTypes:
self.posTypes.append(word[C.POSTAG])
if word[C.DEPREL] not in self.labelTypes:
self.labelTypes.append(word[C.DEPREL])
# This function should take a transition object and apply to the current parser state. It need not return anything.
def execute_transition(self, transition):
if transition.transitionType == Transition.Shift:
self.stack.append(self.buff.pop())
else:
head = self.stack[
-1] if transition.transitionType == Transition.LeftArc else self.stack[
-2]
dependent = self.stack[
-2] if transition.transitionType == Transition.LeftArc else self.stack[
-1]
self.arcs[dependent[C.ID]] = head[C.ID]
self.labels[dependent[C.ID]] = transition.label
self.stack.remove(dependent)
self.wordsThatHaveHeads[dependent[C.ID]] = dependent
self.transitions.append(transition)
@staticmethod
def load_corpus(filename):
print >> sys.stderr, 'Loading treebank from %s' % filename
corpus = []
sentence = []
with open(filename, 'r') as f:
for line in f:
line = line.strip()
if not line:
corpus.append(sentence)
sentence = []
else:
word = line.split('\t')
sentence.append(word)
print >> sys.stderr, 'Loaded %d sentences' % len(corpus)
return corpus
def output(self, sentence):
for word in sentence:
word[C.HEAD] = self.arcs.get(word[C.ID], '0')
label = self.labels.get(word[C.ID], '_')
word[C.DEPREL] = label if label is not None else '_'
print '\t'.join(word)
print
def train(self, trainingSet):
for i in range(self.numIterations):
corpus = Parser.load_corpus(trainingSet)
oracle = Oracle()
for sentence in corpus:
self.initialize(sentence)
self.trackTypes(sentence)
while len(self.buff) > 0 or len(self.stack) > 1:
transition = oracle.getTransition(self.stack, self.buff,
self.dependentIDs,
self.arcs, self.labeled)
self.model.learn(transition, self.stack, self.buff,
self.labels, self.transitions, self.arcs,
self.wordsThatHaveHeads)
self.execute_transition(transition)
def parse(self, testSet):
corpus = Parser.load_corpus(testSet)
for sentence in corpus:
self.initialize(sentence)
while len(self.buff) > 0 or len(self.stack) > 1:
_, transition = self.model.predict(self.stack, self.buff,
self.labels,
self.transitions, self.arcs,
self.wordsThatHaveHeads)
self.execute_transition(transition)
self.output(sentence)
class Parser:
def __init__(self, labeled):
self.labeled = labeled
self.numIterations = 5
self.posTypes = []
self.labelTypes = []
self.model = PerceptronModel(self.labeled)
# self.model = DeepLearningModel(self.labeled, self.posTypes, self.labelTypes)
# self.model = SvmModel(self.labeled)
def initialize(self, sentence):
# ID FORM LEMMA CPOSTAG POSTAG FEATS HEAD DEPREL PHEAD PDEPREL
self.root = ["0", "ROOT", "ROOT", "ROOT", "ROOT", "ROOT", "-1", "ROOT", "ROOT", "ROOT"]
self.buff = [self.root] + list(reversed(sentence)) # buff = [ROOT, N, N-1, ... 1]
self.stack = list()
self.arcs = {} # {dependentId: headId}
self.labels = {} # {dependentId: dependentLabel}
self.wordsThatHaveHeads = {} # {wordId: word}
self.transitions = list()
self.dependentIDs = defaultdict(list) # {headId: [dependentId, ...]}
# Calculate the leftmost and rightmost children for each node in the sentence
# Note: At test time this data is not used.
for word in sentence:
self.dependentIDs[word[C.HEAD]].append(word[C.ID])
# Record types that were seen
def trackTypes(self, sentence):
for word in sentence:
if word[C.POSTAG] not in self.posTypes:
self.posTypes.append(word[C.POSTAG])
if word[C.DEPREL] not in self.labelTypes:
self.labelTypes.append(word[C.DEPREL])
# This function should take a transition object and apply to the current parser state. It need not return anything.
def execute_transition(self, transition):
if transition.transitionType == Transition.Shift:
self.stack.append(self.buff.pop())
else:
head = self.stack[-1] if transition.transitionType == Transition.LeftArc else self.stack[-2]
dependent = self.stack[-2] if transition.transitionType == Transition.LeftArc else self.stack[-1]
self.arcs[dependent[C.ID]] = head[C.ID]
self.labels[dependent[C.ID]] = transition.label
self.stack.remove(dependent)
self.wordsThatHaveHeads[dependent[C.ID]] = dependent
self.transitions.append(transition)
@staticmethod
def load_corpus(filename):
print >> sys.stderr, "Loading treebank from %s" % filename
corpus = []
sentence = []
with open(filename, "r") as f:
for line in f:
line = line.strip()
if not line:
corpus.append(sentence)
sentence = []
else:
word = line.split("\t")
sentence.append(word)
print >> sys.stderr, "Loaded %d sentences" % len(corpus)
return corpus
def output(self, sentence):
for word in sentence:
word[C.HEAD] = self.arcs.get(word[C.ID], "0")
label = self.labels.get(word[C.ID], "_")
word[C.DEPREL] = label if label is not None else "_"
print "\t".join(word)
print
def train(self, trainingSet):
for i in range(self.numIterations):
corpus = Parser.load_corpus(trainingSet)
oracle = Oracle()
for sentence in corpus:
self.initialize(sentence)
self.trackTypes(sentence)
while len(self.buff) > 0 or len(self.stack) > 1:
transition = oracle.getTransition(self.stack, self.buff, self.dependentIDs, self.arcs, self.labeled)
self.model.learn(
transition,
self.stack,
self.buff,
self.labels,
self.transitions,
self.arcs,
self.wordsThatHaveHeads,
)
self.execute_transition(transition)
def parse(self, testSet):
corpus = Parser.load_corpus(testSet)
for sentence in corpus:
self.initialize(sentence)
while len(self.buff) > 0 or len(self.stack) > 1:
_, transition = self.model.predict(
self.stack, self.buff, self.labels, self.transitions, self.arcs, self.wordsThatHaveHeads
)
self.execute_transition(transition)
self.output(sentence)
