def merge(treesFile,graphFile):
trees = load_depTrees_from_file(treesFile)
graphsFromFile = load_depGraphs_from_file(graphFile)
# assert(len(trees)==len(graphsFromFile))
# print "len ok"
graphs = []
for i,t in enumerate(trees):
curGraph,nodesMap = graphsFromFile[i]
curGraph.wsj_id = t[0].wsj_id
curGraph.sent_id = t[0].sent_id
curGraph.originalSentence = t[0].original_sentence
curGraph.wsj_id = t[0].wsj_id
curGraph.sent_id = t[0].sent_id
curGraph.tree_str = "\n".join(t[0].to_original_format().split("\n")[1:])
for node_id in nodesMap:
int_node_id = int(node_id.split("'")[0])
treeNode = t[int_node_id]
child_dic = treeNode._get_child_dic()
if 'cc' in child_dic:
conj_type = (" ".join([cc.word for cc in sorted(child_dic['cc'],key=lambda cc:cc.id)]),[cc.id for cc in child_dic['cc']])
else:
conj_type = False
graphNodes = [nodesMap[n] for n in nodesMap if n.split("'")[0] == node_id]
for graphNode in graphNodes:
graphNode.features = get_verbal_features(treeNode)
if conj_type:
graphNode.features["conjType"] = conj_type
graphNode.features["pos"]=treeNode.pos
graphNode.isPredicate = treeNode.is_verbal_predicate()
# graphNode.original_text = copy.copy(graphNode.text)
graphNode.original_text = treeNode.get_text()
graphNode.surface_form += missing_children(treeNode,graphNode)
curGraph.del_node(nodesMap['0']) # delete root
graphs.append(curGraph)
return graphs
def parseVerbal(self, indexes, verbs, arguments, tree):
"""
add a verbal subgraph to the graph
@type indexes: list [int]
@param indexes: the index(es) of the verb in the sentence
@type verbs: list [string]
@param verbs: the string(s) representing the verb
@type tree: DepTree
@param tree: tree object from which to extract various features
@type arguments: list
@param arguments: list of DepTrees of arguments
"""
# create verbal head node
# start by extracting features
feats = syntactic_item.get_verbal_features(tree)
if feats['Lemma'] == verbs[0]:
del (feats['Lemma'])
for k in feats:
self.types.add(k)
verbNode = graph_representation.node.Node(
isPredicate=True,
text=[
Word(index=index, word=verb)
for index, verb in zip(indexes, verbs)
],
features=feats,
valid=True)
self.gr.add_node(verbNode)
# handle arguments
for arg_t in arguments:
curNode = self.parse(arg_t)
#curNode.features = syntactic_item.get_verbal_features(arg_t)
self.gr.add_edge((verbNode, curNode), arg_t.parent_relation)
# handle time expressions
(timeSubtree, _) = tree._VERBAL_PREDICATE_SUBTREE_Time()
if timeSubtree:
timeNode = graph_representation.node.TimeNode.init(features={})
self.gr.add_node(timeNode)
timeSubGraph = self.parse(timeSubtree)
self.gr.add_edge((verbNode, timeNode))
self.gr.add_edge((timeNode, timeSubGraph))
return verbNode
def treeNode_to_graphNode(treeNode, gr):
"""
@type treeNode DepTree
"""
feats = get_verbal_features(treeNode)
ret = newNode.Node(text=[Word(index=treeNode.id, word=treeNode.word)],
isPredicate=treeNode.is_verbal_predicate(),
features=feats,
gr=gr)
ret.features["pos"] = treeNode.pos
ret.original_text = copy(ret.text)
return ret
def parseVerbal(self,indexes,verbs,arguments,tree):
"""
add a verbal subgraph to the graph
@type indexes: list [int]
@param indexes: the index(es) of the verb in the sentence
@type verbs: list [string]
@param verbs: the string(s) representing the verb
@type tree: DepTree
@param tree: tree object from which to extract various features
@type arguments: list
@param arguments: list of DepTrees of arguments
"""
# create verbal head node
# start by extracting features
feats = syntactic_item.get_verbal_features(tree)
if feats['Lemma'] == verbs[0]:
del(feats['Lemma'])
for k in feats:
self.types.add(k)
verbNode = graph_representation.node.Node(isPredicate=True,
text = [Word(index=index,
word=verb) for index,verb in zip(indexes,verbs)],
features=feats,
valid=True)
self.gr.add_node(verbNode)
# handle arguments
for arg_t in arguments:
curNode = self.parse(arg_t)
#curNode.features = syntactic_item.get_verbal_features(arg_t)
self.gr.add_edge((verbNode,curNode), arg_t.parent_relation)
# handle time expressions
(timeSubtree,_) = tree._VERBAL_PREDICATE_SUBTREE_Time()
if timeSubtree:
timeNode = graph_representation.node.TimeNode.init(features = {})
self.gr.add_node(timeNode)
timeSubGraph = self.parse(timeSubtree)
self.gr.add_edge((verbNode,timeNode))
self.gr.add_edge((timeNode,timeSubGraph))
return verbNode
def treeNode_to_graphNode(treeNode,gr):
"""
@type treeNode DepTree
"""
feats = get_verbal_features(treeNode)
ret = newNode.Node(text = [Word(index=treeNode.id,
word = treeNode.word)],
isPredicate = treeNode.is_verbal_predicate(),
features = feats,
gr = gr)
ret.features["pos"] = treeNode.pos
ret.original_text = copy(ret.text)
return ret
def read_dep_graphs_file(constituency_tree_fn,
wsjInfo_exists=False,
HOME_DIR="./",
stanford_json_sent=None):
stream = convert_json_to_dep_graph(stanford_json_sent) \
if stanford_json_sent \
else convert_to_dep_graph(constituency_tree_fn)
graphsFromFile = create_dep_graphs_from_stream(stream, HOME_DIR)
trees = read_trees_file(constituency_tree_fn, False, stanford_json_sent)
graphs = []
for i, t in enumerate(trees):
curGraph, nodesMap = graphsFromFile[i]
curGraph.originalSentence = t[0].original_sentence
curGraph.tree_str = "\n".join(
t[0].to_original_format().split("\n")[1:])
curGraph.dep_tree = t
for node_id in nodesMap:
int_node_id = int(node_id.split("'")[0])
treeNode = t[int_node_id]
child_dic = treeNode._get_child_dic()
if 'cc' in child_dic:
conj_type = (" ".join([
cc.word
for cc in sorted(child_dic['cc'], key=lambda cc: cc.id)
]), [cc.id for cc in child_dic['cc']])
else:
conj_type = False
graphNodes = [
nodesMap[n] for n in nodesMap if n.split("'")[0] == node_id
]
for graphNode in graphNodes:
graphNode.features = get_verbal_features(treeNode)
if conj_type:
graphNode.features["conjType"] = conj_type
graphNode.features["pos"] = treeNode.pos
graphNode.isPredicate = treeNode.is_verbal_predicate()
# graphNode.original_text = copy.copy(graphNode.text)
graphNode.original_text = treeNode.get_text()
graphNode.surface_form += missing_children(treeNode, graphNode)
curGraph.del_node(nodesMap['0']) # delete root
graphs.append(curGraph)
return graphs
def merge(treesFile, graphFile):
trees = load_depTrees_from_file(treesFile)
graphsFromFile = load_depGraphs_from_file(graphFile)
# assert(len(trees)==len(graphsFromFile))
# print "len ok"
graphs = []
for i, t in enumerate(trees):
curGraph, nodesMap = graphsFromFile[i]
curGraph.wsj_id = t[0].wsj_id
curGraph.sent_id = t[0].sent_id
curGraph.originalSentence = t[0].original_sentence
curGraph.wsj_id = t[0].wsj_id
curGraph.sent_id = t[0].sent_id
curGraph.tree_str = "\n".join(
t[0].to_original_format().split("\n")[1:])
for node_id in nodesMap:
int_node_id = int(node_id.split("'")[0])
treeNode = t[int_node_id]
child_dic = treeNode._get_child_dic()
if 'cc' in child_dic:
conj_type = (" ".join([
cc.word
for cc in sorted(child_dic['cc'], key=lambda cc: cc.id)
]), [cc.id for cc in child_dic['cc']])
else:
conj_type = False
graphNodes = [
nodesMap[n] for n in nodesMap if n.split("'")[0] == node_id
]
for graphNode in graphNodes:
graphNode.features = get_verbal_features(treeNode)
if conj_type:
graphNode.features["conjType"] = conj_type
graphNode.features["pos"] = treeNode.pos
graphNode.isPredicate = treeNode.is_verbal_predicate()
# graphNode.original_text = copy.copy(graphNode.text)
graphNode.original_text = treeNode.get_text()
graphNode.surface_form += missing_children(treeNode, graphNode)
curGraph.del_node(nodesMap['0']) # delete root
graphs.append(curGraph)
return graphs
def parse(self,t):
"""
Get the graph representation from a syntactic representation
Returns through the graph parameter.
@type t: DepTree
@param tree: syntactic tree to be converted
@rtype: Node
@return: the node in the graph corresponding to the top node in t
"""
#order matters!
if t.is_conditional_predicate():
self.types.add(APPENDIX_COND)
return self.parseConditional(outcome = t._CONDITIONAL_PREDICATE_FEATURE_Outcome()["Value"],
condList = t.condPred)
if t._VERBAL_PREDICATE_SUBTREE_Adv():
advChildren = t.adverb_children
advSubj = t.adverb_subj
return self.parseAdverb(subj=advSubj,
advChildren=advChildren)
if t.is_conjunction_predicate():
self.types.add(APPENDIX_CONJUNCTION)
return self.parseConjunction(baseElm = t.baseElm,
conjResult = t.conjResult)
if t.is_appositional_predicate():
self.types.add(APPENDIX_APPOS)
firstEntity = t._APPOSITIONAL_PREDICATE_FEATURE_Left_Side()["Value"]
secondEntity = t._APPOSITIONAL_PREDICATE_FEATURE_Right_Side()["Value"]
return self.parseApposition(index = t.id,
first_entity=firstEntity,
second_entity=secondEntity)
if t.is_relative_clause():
self.types.add(APPENDIX_RCMOD)
return self.parseRcmod(np = t._RELCLAUSE_PREDICATE_FEATURE_Rest()['Value'],
modList = t.rcmodPred)
if t.is_prepositional_predicate():
self.types.add(APPENDIX_PREP)
return self.parsePreposition(psubj=t._PREPOSITIONAL_PREDICATE_FEATURE_psubj()["Value"],
prepChildList=t.prepChildList)
if t.is_copular_predicate():
self.types.add(APPENDIX_COP)
firstEntity = t._COPULAR_PREDICATE_FEATURE_Copular_Predicate()["Value"]
secondEntity = t._COPULAR_PREDICATE_FEATURE_Copular_Object()["Value"]
return self.parseCopular(index = t.id,
first_entity=firstEntity,
second_entity=secondEntity,
features = syntactic_item.get_verbal_features(t))
if t.is_possesive_predicate():
self.types.add(APPENDIX_POSS)
possessor = t._POSSESSIVE_PREDICATE_FEATURE_Possessor()["Value"]
possessed = t._POSSESSIVE_PREDICATE_FEATURE_Possessed()["Value"]
possessive = t._POSSESSIVE_PREDICATE_FEATURE_Possessive()["Value"]
return self.parsePossessive(possessor = possessor,
possessed = possessed,
possessive = possessive)
if t.is_adjectival_predicate():
self.types.add(APPENDIX_ADJ)
return self.parseProp(subject = t._ADJECTIVAL_PREDICATE_FEATURE_Subject()["Value"],
copulaIndex = NO_INDEX,
adjectiveChildList = t.adjectivalChildList,
propAsHead=False)
if t.is_clausal_complement():
self.types.add(APPENDIX_COMPLEMENT)
return self.parseComplement(compSubj = t.compSubj,
compChildren = t.compChildList)
if t.unhandled_advcl():
# put each unhandled advcl as a disconnected subgraph
for c in t.advcl:
self.parse(c)
return self.parse(t)
if t.is_verbal_predicate():
self.types.add(APPENDIX_VERB)
head_ret = t._VERBAL_PREDICATE_SUBTREE_Head()
return self.parseVerbal(indexes = head_ret["Span"],
verbs = head_ret["Value"].split(" "),
arguments = t.collect_arguments(),
tree = t)
else:
# fall back - pack all the tree in a single node
if len(t.children)==1:
if (t.children[0].parent_relation == "nn") and (t.word.endswith(",")) and (t.children[0].word.endswith(",")):
#conjunction in disguise
child = t.children[0]
t.children = []
ret = self.parseConjunction(cc = [(t.id,"and")],
conjElements = [t,child])
t.children = [child]
return ret
nodes = t._get_subtree(filter_labels_ban)
text = [Word(index=index,
word=nodes[index]) for index in sorted(nodes.keys())]
topNode = self.parseBottom(text = sorted(text,key=lambda x:x.index),
features = syntactic_item.get_verbal_features(t))
return topNode
def parse(self,t):
"""
Get the graph representation from a syntactic representation
Returns through the graph parameter.
@type t: DepTree
@param tree: syntactic tree to be converted
@rtype: Node
@return: the node in the graph corresponding to the top node in t
"""
#order matters!
if t.is_conditional_predicate():
self.types.add(APPENDIX_COND)
return self.parseConditional(outcome = t._CONDITIONAL_PREDICATE_FEATURE_Outcome()["Value"],
condList = t.condPred)
if t._VERBAL_PREDICATE_SUBTREE_Adv():
advChildren = t.adverb_children
advSubj = t.adverb_subj
return self.parseAdverb(subj=advSubj,
advChildren=advChildren)
if t.is_conjunction_predicate():
self.types.add(APPENDIX_CONJUNCTION)
return self.parseConjunction(baseElm = t.baseElm,
conjResult = t.conjResult)
if t.is_appositional_predicate():
self.types.add(APPENDIX_APPOS)
firstEntity = t._APPOSITIONAL_PREDICATE_FEATURE_Left_Side()["Value"]
secondEntity = t._APPOSITIONAL_PREDICATE_FEATURE_Right_Side()["Value"]
return self.parseApposition(index = t.id,
first_entity=firstEntity,
second_entity=secondEntity)
if t.is_relative_clause():
self.types.add(APPENDIX_RCMOD)
return self.parseRcmod(np = t._RELCLAUSE_PREDICATE_FEATURE_Rest()['Value'],
modList = t.rcmodPred)
if t.is_prepositional_predicate():
self.types.add(APPENDIX_PREP)
return self.parsePreposition(psubj=t._PREPOSITIONAL_PREDICATE_FEATURE_psubj()["Value"],
prepChildList=t.prepChildList)
if t.is_copular_predicate():
self.types.add(APPENDIX_COP)
firstEntity = t._COPULAR_PREDICATE_FEATURE_Copular_Predicate()["Value"]
secondEntity = t._COPULAR_PREDICATE_FEATURE_Copular_Object()["Value"]
return self.parseCopular(index = t.id,
first_entity=firstEntity,
second_entity=secondEntity,
features = syntactic_item.get_verbal_features(t))
if t.is_possesive_predicate():
self.types.add(APPENDIX_POSS)
possessor = t._POSSESSIVE_PREDICATE_FEATURE_Possessor()["Value"]
possessed = t._POSSESSIVE_PREDICATE_FEATURE_Possessed()["Value"]
possessive = t._POSSESSIVE_PREDICATE_FEATURE_Possessive()["Value"]
return self.parsePossessive(possessor = possessor,
possessed = possessed,
possessive = possessive)
if t.is_adjectival_predicate():
self.types.add(APPENDIX_ADJ)
return self.parseProp(subject = t._ADJECTIVAL_PREDICATE_FEATURE_Subject()["Value"],
copulaIndex = NO_INDEX,
adjectiveChildList = t.adjectivalChildList,
propAsHead=False)
if t.is_clausal_complement():
self.types.add(APPENDIX_COMPLEMENT)
return self.parseComplement(compSubj = t.compSubj,
compChildren = t.compChildList)
if t.unhandled_advcl():
# put each unhandled advcl as a disconnected subgraph
for c in t.advcl:
self.parse(c)
return self.parse(t)
if t.is_verbal_predicate():
self.types.add(APPENDIX_VERB)
head_ret = t._VERBAL_PREDICATE_SUBTREE_Head()
return self.parseVerbal(indexes = head_ret["Span"],
verbs = head_ret["Value"].split(" "),
arguments = t.collect_arguments(),
tree = t)
else:
# fall back - pack all the tree in a single node
if len(t.children)==1:
if (t.children[0].parent_relation == "nn") and (t.word.endswith(",")) and (t.children[0].word.endswith(",")):
#conjunction in disguise
child = t.children[0]
t.children = []
ret = self.parseConjunction(cc = [(t.id,"and")],
conjElements = [t,child])
t.children = [child]
return ret
nodes = t._get_subtree(filter_labels_ban)
text = [Word(index=index,
word=nodes[index]) for index in sorted(nodes.keys())]
topNode = self.parseBottom(text = sorted(text,key=lambda x:x.index),
features = syntactic_item.get_verbal_features(t))
return topNode
