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amrdata.py
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#!/usr/bin/env python
#coding=utf-8
'''
AMRDataset reads the file generated by preprocessing.sh and it generates a AMRSentence instance for each sentence,
containing all information necessary to the parser.
@author: Marco Damonte (m.damonte@sms.ed.ac.uk)
@since: 3-10-16
'''
import re
from alignments import Alignments as Alignments
import sys
import amr as amrannot
class AMRSentence:
def __init__(self, tokens, pos, lemmas, nes, dependencies, variables = None, relations = None, graph = None, alignments = None):
self.tokens = tokens
self.pos = pos
self.lemmas = lemmas
self.nes = nes
self.dependencies = dependencies
if variables is not None:
self.variables = [(str(k),str(variables[k])) for k in variables]
if relations is not None:
self.relations = [r for r in relations if r[0] != r[2]]
self.graph = graph
self.alignments = alignments
class AMRDataset:
def _var2concept(self, amr):
v2c = {}
for n, v in zip(amr.nodes, amr.node_values):
v2c[n] = v
return v2c
def __init__(self, prefix, amrs, normalize = True):
self.normalize = normalize
self.sentences = []
alltokens, allpos, alllemmas, allnes, alldepslines = self._loadFromFile(prefix + ".out")
if amrs:
allgraphs = open(prefix + ".graphs").read().split("\n\n")
a = Alignments(prefix + ".alignments", allgraphs)
allalignments = a.alignments
for graph, alignments, depslines, tokens, pos, lemmas, nes in zip(allgraphs, allalignments, alldepslines, alltokens, allpos, alllemmas, allnes):
graph = graph.strip()
amr = amrannot.AMR.parse_AMR_line(graph.replace("\n",""), False)
variables = {}
for n, v in zip(amr.nodes, amr.node_values):
variables[n] = v
role_triples = amr.get_triples3()
relations = []
for (var1,label,var2) in role_triples:
if label == "TOP":
relations.append(("TOP",":top",var1))
else:
relations.append((str(var1),":" + str(label),str(var2)))
dependencies = []
for line in depslines.split("\n"):
pattern = "^(.+)\(.+-([0-9]+), .+-([0-9]+)\)"
regex = re.match(pattern, line)
if regex is not None:
label = regex.group(1)
a = int(regex.group(2)) - 1
b = int(regex.group(3)) - 1
if a == -1:
dependencies.append((b, 'ROOT', b))
elif a != b:
dependencies.append((a, label, b))
self.sentences.append(AMRSentence(tokens, pos, lemmas, nes, dependencies, variables, relations, graph, alignments))
else:
for depslines, tokens, pos, lemmas, nes in zip(alldepslines, alltokens, allpos, alllemmas, allnes):
dependencies = []
for line in depslines.split("\n"):
pattern = "^(.+)\(.+-([0-9]+), .+-([0-9]+)\)"
regex = re.match(pattern, line)
if regex is not None:
label = regex.group(1)
a = int(regex.group(2)) - 1
b = int(regex.group(3)) - 1
if a == -1:
dependencies.append((b, 'ROOT', b))
elif a != b:
dependencies.append((a, label, b))
self.sentences.append(AMRSentence(tokens, pos, lemmas, nes, dependencies))
def getSent(self, index):
return self.sentences[index]
def getAllSents(self):
return self.sentences
def _loadFromFile(self, stanfordOutput, norm = True):
alltokens = []
allpos = []
alllemmas = []
allnes = []
alldepslines = []
blocks = open(stanfordOutput, 'r').read().split("\n\n")
while True:
if len(blocks) == 1:
break
block = blocks.pop(0).strip().split("\n")
tokens = []
lemmas = []
nes = []
pos = []
i = 2
while block[i].startswith("[Text"):
tokens.extend([t[5:-1] for t in re.findall('Text=[^\s]* ', block[i])])
pos.extend([t[13:-1] for t in re.findall('PartOfSpeech=[^\s]* ', block[i])])
lemmas.extend([t[6:-1] for t in re.findall('Lemma=[^\s]* ', block[i])])
nes.extend([t[15:] for t in re.findall('NamedEntityTag=[^\]]*', block[i])])
i += 1
allpos.append(pos)
if blocks[0].startswith("\n"):
b = ""
else:
b = blocks.pop(0)
depslines = b
alltokens.append(tokens)
alllemmas.append(lemmas)
allnes.append(nes)
alldepslines.append(depslines)
continue # don't need this part of evaluation....
#very messy piece of code to handle corenlp normalization (for dates, currencies, etc)
tokens2 = []
lemmas2 = []
nes2 = []
lastnorm = None
for token, lemma, ne in zip(tokens, lemmas, nes):
nesplit = ne.split()
if len(nesplit) > 1:
mne = re.match("^([a-zA-Z\%\>\<\$\~\=]*)([0-9\.]*.*)", nesplit[1][25:].encode('ascii', 'ignore'))
else:
mne = None
if nesplit[0] == "DATE" and re.match("^(\d{4}|XXXX)(-\d{2})?(-\d{2})?$",nesplit[1][25:]) is not None:
norm = nesplit[1][25:]
lastnorm = norm
tokens2.append(norm)
lemmas2.append(norm)
nes2.append(nesplit[0])
elif (nesplit[0] == "MONEY" or nesplit[0] == "PERCENT") and self.normalize and len(nesplit) == 2 and mne is not None:
[name, norm] = nesplit
curr = mne.groups()[0]
norm = mne.groups()[1]
curr = curr.replace("<","").replace(">","").replace("~","").replace("=","")
if curr == "$":
curr = "dollar"
if curr == "":
w = nesplit[1][25:].replace("<","").replace(">","").replace("~","").replace("=","")
if w.startswith(u"\u00A5"):
curr = "yen"
elif w.startswith(u"\u5143"):
curr = "yuan"
elif w.startswith(u"\u00A3"):
curr = "pound"
elif w.startswith(u"\u20AC"):
curr = "euro"
else:
curr = "NULL"
m = re.match("([0-9\.][0-9\.]*)E([0-9][0-9]*)$",norm)
if m is not None:
n = m.groups()[0]
z = "".join(["0"]*int(m.groups()[1]))
norm = format(float(n)*float("1"+z), ".32f")
norm = re.sub("\.00*$","",norm)
if token.endswith(".0") == False:
norm = re.sub("\.0$","",norm)
if token.replace(",","").replace(".","").isdigit() == False and lastnorm is not None:
norm = ","
token = ","
name = "O"
lastnorm = norm
if norm == ",":
tokens2.append(norm)
else:
tokens2.append(norm + "_" + curr)
lemmas2.append(token)
nes2.append(name)
elif self.normalize and len(nesplit) == 2 and re.match("^[0-9].*", nesplit[1][25:]) is not None: #numbers
[name, norm] = nesplit
norm = norm[25:]
m = re.match("([0-9\.][0-9\.]*)E([0-9][0-9]*)$",norm)
if m is not None:
n = m.groups()[0]
z = "".join(["0"]*int(m.groups()[1]))
norm = str(float(n)*int("1"+z))
if token.endswith(".0") == False:
norm = re.sub("\.0$","",norm)
if token.replace(",","").replace(".","").isdigit() == False and lastnorm is not None:
norm = ","
token = ","
name = "O"
lastnorm = norm
tokens2.append(norm)
lemmas2.append(token)
nes2.append(name)
else:
lastnorm = None
tokens2.append(token)
lemmas2.append(lemma)
nes2.append(nesplit[0])
alltokens.append(tokens2)
alllemmas.append(lemmas2)
allnes.append(nes2)
alldepslines.append(depslines)
return (alltokens, allpos, alllemmas, allnes, alldepslines)