def loadFile(input_filename):
"""
load AMR parsed file, re-index AMR nodes and edges.
return corpus of nodes and edges
"""
graph_str = '' # AMR parse
info_dict = {} # AMR meta info
doc_filename = ''
corpus = {} # filename -> (nodes, root_nodes, edges, exp_edges)
doc_nodes = {} # (concept,) -> AmrNode
doc_root_nodes = {} # (concept,) -> AmrNode
doc_edges = {} # (concept1, concept2) -> AmrEdge
doc_exp_edges = {} # (concept1, concept2) -> AmrEdge
with codecs.open(input_filename, 'r', 'utf-8') as infile:
for line in infile:
line = line.rstrip()
if line == '':
# no AMR graph for current sentence
if graph_str == '':
info_dict = {}
continue
# get nodes and edges (linked)
g = AmrGraph()
nodes, edges = g.getCollapsedNodesAndEdges(graph_str.split())
# index nodes by graph_idx
node_indices = {}
for node in nodes:
graph_idx = node.graph_idx
node_indices.setdefault(graph_idx, node)
# (1) use gold AMR annotation as input
if not 'alignments' in info_dict:
# get sentence info
sentence = info_dict['snt'] # tokenized sentence
filename, line_num = info_dict['id'].split('.')
line_num = int(line_num)
# add source info to nodes
for node in nodes:
node_source = NodeSource(node.graph_idx, 0, 0, '',
filename, line_num, sentence)
node.sources.append(node_source)
# add source info to edges
for edge in edges:
edge_source = EdgeSource(edge.relation, filename,
line_num, sentence)
edge.sources.append(edge_source)
else: # (2) use alignment file as input
# get sentence info
sentence = info_dict['tok'] # tokenized sentence
tokens = sentence.split()
filename, line_num = info_dict['id'].split('.')
line_num = int(line_num)
# add source info to edges
for edge in edges:
edge_source = EdgeSource(edge.relation, filename,
line_num, sentence)
edge.sources.append(edge_source)
# add alignment and other source info to nodes
alignments_str = info_dict['alignments']
for alignment in alignments_str.split():
word_part, graph_part = alignment.split('|')
start_idx, end_idx = map(int, word_part.split('-'))
graph_indices = graph_part.split('+')
for graph_idx in graph_indices:
curr_node = node_indices.get(graph_idx, None)
if curr_node is None: continue
# add node source info
new_start_idx = start_idx
new_end_idx = end_idx
# change existing start_idx/end_idx to broadest coverage
if curr_node.sources:
curr_node_source = curr_node.sources.pop()
if new_start_idx > curr_node_source.start_idx:
new_start_idx = curr_node_source.start_idx
if new_end_idx < curr_node_source.end_idx:
new_end_idx = curr_node_source.end_idx
# update new node source
new_node_source = NodeSource(
curr_node.graph_idx, new_start_idx,
new_end_idx,
' '.join(tokens[new_start_idx:new_end_idx]),
filename, line_num, sentence)
curr_node.sources.append(new_node_source)
# add source info to [unaligned] nodes
for node in nodes:
if node.sources: continue
node_source = NodeSource(node.graph_idx, 0, 0, '',
filename, line_num, sentence)
node.sources.append(node_source)
# start of new file
if filename != doc_filename:
if doc_filename != '':
corpus[doc_filename] = (doc_nodes, doc_root_nodes,
doc_edges, doc_exp_edges)
doc_filename = filename
doc_nodes = {}
doc_root_nodes = {}
doc_edges = {}
doc_exp_edges = {}
# keep track of redirected nodes
redirect_dict = {}
# merge nodes
first_node = True
for node in nodes:
curr_anchor = tuple((node.concept, )) # tricky tuple
if curr_anchor in doc_nodes:
old_node = doc_nodes[curr_anchor]
old_node.sources.extend(node.sources)
redirect_dict[node] = old_node
else:
doc_nodes[curr_anchor] = node
# root node of sentence
if first_node == True:
doc_root_nodes[curr_anchor] = doc_nodes[curr_anchor]
first_node = False
# merge edges
edge_indices = {} # index edge by concepts
for edge in edges:
# update node linkage
if edge.node1 in redirect_dict:
edge.node1 = redirect_dict[edge.node1]
if edge.node2 in redirect_dict:
edge.node2 = redirect_dict[edge.node2]
curr_anchor = tuple(
(edge.node1.concept,
edge.node2.concept)) # ignore relation
edge_indices[curr_anchor] = edge
if curr_anchor in doc_edges:
old_edge = doc_edges[curr_anchor]
old_edge.sources.extend(edge.sources)
else:
doc_edges[curr_anchor] = edge
# expand edges, nodes in each sentence are fully connected
for node1 in nodes:
for node2 in nodes:
curr_anchor = tuple((node1.concept, node2.concept))
redirect_node1 = doc_nodes[(node1.concept, )]
redirect_node2 = doc_nodes[(node2.concept, )]
# expanded edge exists
if curr_anchor in doc_exp_edges:
# update node linkage
old_edge = doc_exp_edges[curr_anchor]
old_edge.node1 = redirect_node1
old_edge.node2 = redirect_node2
# update edge sources
if curr_anchor in edge_indices: # true edge
edge = edge_indices[curr_anchor]
old_edge.sources.extend(edge.sources)
else: # NULL edge
edge_source = EdgeSource(
'NULL', filename, line_num, sentence)
old_edge.sources.append(edge_source)
else: # expanded edge does not exist, build a new edge
if curr_anchor in edge_indices: # true edge
edge = edge_indices[curr_anchor]
new_edge = AmrEdge(node1=redirect_node1,
node2=redirect_node2,
relation=edge.relation)
new_edge.sources.extend(edge.sources)
else: # NULL edge
new_edge = AmrEdge(node1=redirect_node1,
node2=redirect_node2,
relation='NULL')
edge_source = EdgeSource(
'NULL', filename, line_num, sentence)
new_edge.sources.append(edge_source)
doc_exp_edges[curr_anchor] = new_edge
# clear cache
graph_str = ''
info_dict = {}
continue
if line.startswith('#'):
fields = line.split('::')
for field in fields[1:]:
tokens = field.split()
info_name = tokens[0]
info_body = ' '.join(tokens[1:])
info_dict[info_name] = info_body
continue
graph_str += line
# add nodes and edges to the last file
corpus[doc_filename] = (doc_nodes, doc_root_nodes, doc_edges,
doc_exp_edges)
# return loaded corpus
return corpus
def getTriples(input_file):
"""
get triples from gold-standard AMR annotations
"""
triples = defaultdict(list)
extended_triples = defaultdict(list)
concepts = defaultdict(Counter) # Counter
graph_string = '' # AMR graph
tokens = '' # current tokens
line_num = -1 # line_num in file
filename = '' # current filename
with codecs.open(input_file, 'r', 'utf-8') as infile:
for line in infile:
line = line.rstrip()
# process each graph
if line == '' and graph_string:
g = AmrGraph()
graph_tokens = graph_string.split()
nodes, edges = g.getCollapsedNodesAndEdges(graph_tokens)
# nodes = g.getNodes(graph_tokens)
# edges = g.getEdges(graph_tokens)
# get triples
for edge in edges:
node1 = edge.node1
node2 = edge.node2
relation = edge.relation
t = Triple(node1.concept, node2.concept, relation,
filename, line_num, tokens)
triples[filename].append(t)
# get extended triples (ignore relation, but keep direction)
for node1 in nodes:
for node2 in nodes:
t = Triple(node1.concept, node2.concept, '', filename,
line_num, tokens)
extended_triples[filename].append(t)
# get concepts
for node in nodes:
concepts[filename][node.concept] += 1
# clear cache
graph_string = ''
filename = ''
line_num = -1
tokens = ''
continue
if line.startswith('# ::id'):
tokens = line.split()
filename, line_num = tokens[2].split('.')
line_num = int(line_num)
continue
# get snt-type: summary, body, etc.
if line.startswith('# ::snt'):
tokens = line
continue
# get AMR graph string
if line and not line.startswith('#'):
graph_string += line
continue
return triples, extended_triples, concepts
def loadFile(input_filename):
"""
load AMR parsed file, re-index AMR nodes and edges.
return corpus of nodes and edges
"""
graph_str = '' # AMR parse
info_dict = {} # AMR meta info
doc_filename = ''
corpus = {} # filename -> (nodes, root_nodes, edges, exp_edges)
doc_nodes = {} # (concept,) -> AmrNode
doc_root_nodes = {} # (concept,) -> AmrNode
doc_edges = {} # (concept1, concept2) -> AmrEdge
doc_exp_edges = {} # (concept1, concept2) -> AmrEdge
with codecs.open(input_filename, 'r', 'utf-8') as infile:
for line in infile:
line = line.rstrip()
if line == '':
# no AMR graph for current sentence
if graph_str == '':
info_dict = {}
continue
# get nodes and edges (linked)
g = AmrGraph()
nodes, edges = g.getCollapsedNodesAndEdges(graph_str.split())
# index nodes by graph_idx
node_indices = {}
for node in nodes:
graph_idx = node.graph_idx
node_indices.setdefault(graph_idx, node)
# (1) use gold AMR annotation as input
if not 'alignments' in info_dict:
# get sentence info
sentence = info_dict['snt'] # tokenized sentence
filename, line_num = info_dict['id'].split('.')
line_num = int(line_num)
# add source info to nodes
for node in nodes:
node_source = NodeSource(node.graph_idx, 0, 0, '', filename, line_num, sentence)
node.sources.append(node_source)
# add source info to edges
for edge in edges:
edge_source = EdgeSource(edge.relation, filename, line_num, sentence)
edge.sources.append(edge_source)
else: # (2) use alignment file as input
# get sentence info
sentence = info_dict['tok'] # tokenized sentence
tokens = sentence.split()
filename, line_num = info_dict['id'].split('.')
line_num = int(line_num)
# add source info to edges
for edge in edges:
edge_source = EdgeSource(edge.relation, filename, line_num, sentence)
edge.sources.append(edge_source)
# add alignment and other source info to nodes
alignments_str = info_dict['alignments']
for alignment in alignments_str.split():
word_part, graph_part = alignment.split('|')
start_idx, end_idx = map(int, word_part.split('-'))
graph_indices = graph_part.split('+')
for graph_idx in graph_indices:
curr_node = node_indices.get(graph_idx, None)
if curr_node is None: continue
# add node source info
new_start_idx = start_idx
new_end_idx = end_idx
# change existing start_idx/end_idx to broadest coverage
if curr_node.sources:
curr_node_source = curr_node.sources.pop()
if new_start_idx > curr_node_source.start_idx:
new_start_idx = curr_node_source.start_idx
if new_end_idx < curr_node_source.end_idx:
new_end_idx = curr_node_source.end_idx
# update new node source
new_node_source = NodeSource(curr_node.graph_idx, new_start_idx, new_end_idx,
' '.join(tokens[new_start_idx:new_end_idx]),
filename, line_num, sentence)
curr_node.sources.append(new_node_source)
# add source info to [unaligned] nodes
for node in nodes:
if node.sources: continue
node_source = NodeSource(node.graph_idx, 0, 0, '', filename, line_num, sentence)
node.sources.append(node_source)
# start of new file
if filename != doc_filename:
if doc_filename != '':
corpus[doc_filename] = (doc_nodes, doc_root_nodes, doc_edges, doc_exp_edges)
doc_filename = filename
doc_nodes = {}
doc_root_nodes = {}
doc_edges = {}
doc_exp_edges = {}
# keep track of redirected nodes
redirect_dict = {}
# merge nodes
first_node = True
for node in nodes:
curr_anchor = tuple((node.concept,)) # tricky tuple
if curr_anchor in doc_nodes:
old_node = doc_nodes[curr_anchor]
old_node.sources.extend(node.sources)
redirect_dict[node] = old_node
else:
doc_nodes[curr_anchor] = node
# root node of sentence
if first_node == True:
doc_root_nodes[curr_anchor] = doc_nodes[curr_anchor]
first_node = False
# merge edges
edge_indices = {} # index edge by concepts
for edge in edges:
# update node linkage
if edge.node1 in redirect_dict:
edge.node1 = redirect_dict[edge.node1]
if edge.node2 in redirect_dict:
edge.node2 = redirect_dict[edge.node2]
curr_anchor = tuple((edge.node1.concept, edge.node2.concept)) # ignore relation
edge_indices[curr_anchor] = edge
if curr_anchor in doc_edges:
old_edge = doc_edges[curr_anchor]
old_edge.sources.extend(edge.sources)
else:
doc_edges[curr_anchor] = edge
# expand edges, nodes in each sentence are fully connected
for node1 in nodes:
for node2 in nodes:
curr_anchor = tuple((node1.concept, node2.concept))
redirect_node1 = doc_nodes[(node1.concept,)]
redirect_node2 = doc_nodes[(node2.concept,)]
# expanded edge exists
if curr_anchor in doc_exp_edges:
# update node linkage
old_edge = doc_exp_edges[curr_anchor]
old_edge.node1 = redirect_node1
old_edge.node2 = redirect_node2
# update edge sources
if curr_anchor in edge_indices: # true edge
edge = edge_indices[curr_anchor]
old_edge.sources.extend(edge.sources)
else: # NULL edge
edge_source = EdgeSource('NULL', filename, line_num, sentence)
old_edge.sources.append(edge_source)
else: # expanded edge does not exist, build a new edge
if curr_anchor in edge_indices: # true edge
edge = edge_indices[curr_anchor]
new_edge = AmrEdge(node1=redirect_node1, node2=redirect_node2, relation=edge.relation)
new_edge.sources.extend(edge.sources)
else: # NULL edge
new_edge = AmrEdge(node1=redirect_node1, node2=redirect_node2, relation='NULL')
edge_source = EdgeSource('NULL', filename, line_num, sentence)
new_edge.sources.append(edge_source)
doc_exp_edges[curr_anchor] = new_edge
# clear cache
graph_str = ''
info_dict = {}
continue
if line.startswith('#'):
fields = line.split('::')
for field in fields[1:]:
tokens = field.split()
info_name = tokens[0]
info_body = ' '.join(tokens[1:])
info_dict[info_name] = info_body
continue
graph_str += line
# add nodes and edges to the last file
corpus[doc_filename] = (doc_nodes, doc_root_nodes, doc_edges, doc_exp_edges)
# return loaded corpus
return corpus
def getTriples(input_file):
"""
get triples from gold-standard AMR annotations
"""
triples = defaultdict(list)
extended_triples = defaultdict(list)
concepts = defaultdict(Counter) # Counter
graph_string = "" # AMR graph
tokens = "" # current tokens
line_num = -1 # line_num in file
filename = "" # current filename
with codecs.open(input_file, "r", "utf-8") as infile:
for line in infile:
line = line.rstrip()
# process each graph
if line == "" and graph_string:
g = AmrGraph()
graph_tokens = graph_string.split()
nodes, edges = g.getCollapsedNodesAndEdges(graph_tokens)
# nodes = g.getNodes(graph_tokens)
# edges = g.getEdges(graph_tokens)
# get triples
for edge in edges:
node1 = edge.node1
node2 = edge.node2
relation = edge.relation
t = Triple(node1.concept, node2.concept, relation, filename, line_num, tokens)
triples[filename].append(t)
# get extended triples (ignore relation, but keep direction)
for node1 in nodes:
for node2 in nodes:
t = Triple(node1.concept, node2.concept, "", filename, line_num, tokens)
extended_triples[filename].append(t)
# get concepts
for node in nodes:
concepts[filename][node.concept] += 1
# clear cache
graph_string = ""
filename = ""
line_num = -1
tokens = ""
continue
if line.startswith("# ::id"):
tokens = line.split()
filename, line_num = tokens[2].split(".")
line_num = int(line_num)
continue
# get snt-type: summary, body, etc.
if line.startswith("# ::snt"):
tokens = line
continue
# get AMR graph string
if line and not line.startswith("#"):
graph_string += line
continue
return triples, extended_triples, concepts
