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from oie_readers.argument import Argument
from operator import itemgetter
from collections import defaultdict
import nltk
import itertools
import logging
import numpy as np
class Extraction:
"""
Stores sentence, single predicate and corresponding arguments.
"""
def __init__(self, pred, head_pred_index, sent, confidence, question_dist = '', index = -1):
self.pred = pred
self.head_pred_index = head_pred_index
self.sent = sent
self.args = []
self.confidence = confidence
self.matched = []
self.questions = {}
self.indsForQuestions = defaultdict(lambda: set())
self.is_mwp = False
self.question_dist = question_dist
self.index = index
def distArgFromPred(self, arg):
assert(len(self.pred) == 2)
dists = []
for x in self.pred[1]:
for y in arg.indices:
dists.append(abs(x - y))
return min(dists)
def argsByDistFromPred(self, question):
return sorted(self.questions[question], key = lambda arg: self.distArgFromPred(arg))
def addArg(self, arg, question = None):
self.args.append(arg)
if question:
self.questions[question] = self.questions.get(question,[]) + [Argument(arg)]
def noPronounArgs(self):
"""
Returns True iff all of this extraction's arguments are not pronouns.
"""
for (a, _) in self.args:
tokenized_arg = nltk.word_tokenize(a)
if len(tokenized_arg) == 1:
_, pos_tag = nltk.pos_tag(tokenized_arg)[0]
if ('PRP' in pos_tag):
return False
return True
def isContiguous(self):
return all([indices for (_, indices) in self.args])
def toBinary(self):
''' Try to represent this extraction's arguments as binary
If fails, this function will return an empty list. '''
ret = [self.elementToStr(self.pred)]
if len(self.args) == 2:
# we're in luck
return ret + [self.elementToStr(arg) for arg in self.args]
return []
if not self.isContiguous():
# give up on non contiguous arguments (as we need indexes)
return []
# otherwise, try to merge based on indices
# TODO: you can explore other methods for doing this
binarized = self.binarizeByIndex()
if binarized:
return ret + binarized
return []
def elementToStr(self, elem, print_indices = True):
''' formats an extraction element (pred or arg) as a raw string
removes indices and trailing spaces '''
if print_indices:
return str(elem)
if isinstance(elem, str):
return elem
if isinstance(elem, tuple):
ret = elem[0].rstrip().lstrip()
else:
ret = ' '.join(elem.words)
assert ret, "empty element? {0}".format(elem)
return ret
def binarizeByIndex(self):
extraction = [self.pred] + self.args
markPred = [(w, ind, i == 0) for i, (w, ind) in enumerate(extraction)]
sortedExtraction = sorted(markPred, key = lambda ws, indices, f : indices[0])
s = ' '.join(['{1} {0} {1}'.format(self.elementToStr(elem), SEP) if elem[2] else self.elementToStr(elem) for elem in sortedExtraction])
binArgs = [a for a in s.split(SEP) if a.rstrip().lstrip()]
if len(binArgs) == 2:
return binArgs
# failure
return []
def bow(self):
return ' '.join([self.elementToStr(elem) for elem in [self.pred] + self.args])
def getSortedArgs(self):
"""
Sort the list of arguments.
If a question distribution is provided - use it,
otherwise, default to the order of appearance in the sentence.
"""
if self.question_dist:
# There's a question distribtuion - use it
return self.sort_args_by_distribution()
ls = []
for q, args in self.questions.iteritems():
if (len(args) != 1):
logging.debug("Not one argument: {}".format(args))
continue
arg = args[0]
indices = list(self.indsForQuestions[q].union(arg.indices))
if not indices:
logging.debug("Empty indexes for arg {} -- backing to zero".format(arg))
indices = [0]
ls.append(((arg, q), indices))
return [a for a, _ in sorted(ls,
key = lambda _, indices: min(indices))]
def question_prob_for_loc(self, question, loc):
"""
Returns the probability of the given question leading to argument
appearing in the given location in the output slot.
"""
gen_question = generalize_question(question)
q_dist = self.question_dist[gen_question]
logging.debug("distribution of {}: {}".format(gen_question,
q_dist))
return float(q_dist.get(loc, 0)) / \
sum(q_dist.values())
def sort_args_by_distribution(self):
"""
Use this instance's question distribution (this func assumes it exists)
in determining the positioning of the arguments.
Greedy algorithm:
0. Decide on which argument will serve as the ``subject'' (first slot) of this extraction
0.1 Based on the most probable one for this spot
(special care is given to select the highly-influential subject position)
1. For all other arguments, sort arguments by the prevalance of their questions
2. For each argument:
2.1 Assign to it the most probable slot still available
2.2 If non such exist (fallback) - default to put it in the last location
"""
INF_LOC = 100 # Used as an impractical last argument
# Store arguments by slot
ret = {INF_LOC: []}
logging.debug("sorting: {}".format(self.questions))
# Find the most suitable arguemnt for the subject location
logging.debug("probs for subject: {}".format([(q, self.question_prob_for_loc(q, 0))
for (q, _) in self.questions.iteritems()]))
subj_question, subj_args = max(self.questions.iteritems(),
key = lambda q, _: self.question_prob_for_loc(q, 0))
ret[0] = [(subj_args[0], subj_question)]
# Find the rest
for (question, args) in sorted([(q, a)
for (q, a) in self.questions.iteritems() if (q not in [subj_question])],
key = lambda q, _: \
sum(self.question_dist[generalize_question(q)].values()),
reverse = True):
gen_question = generalize_question(question)
arg = args[0]
assigned_flag = False
for (loc, count) in sorted(self.question_dist[gen_question].iteritems(),
key = lambda _ , c: c,
reverse = True):
if loc not in ret:
# Found an empty slot for this item
# Place it there and break out
ret[loc] = [(arg, question)]
assigned_flag = True
break
if not assigned_flag:
# Add this argument to the non-assigned (hopefully doesn't happen much)
logging.debug("Couldn't find an open assignment for {}".format((arg, gen_question)))
ret[INF_LOC].append((arg, question))
logging.debug("Linearizing arg list: {}".format(ret))
# Finished iterating - consolidate and return a list of arguments
return [arg
for (_, arg_ls) in sorted(ret.iteritems(),
key = lambda k, v: int(k))
for arg in arg_ls]
def __str__(self):
pred_str = self.elementToStr(self.pred)
return '{}\t{}\t{}'.format(self.get_base_verb(pred_str),
self.compute_global_pred(pred_str,
self.questions.keys()),
'\t'.join([escape_special_chars(self.augment_arg_with_question(self.elementToStr(arg),
question))
for arg, question in self.getSortedArgs()]))
def get_base_verb(self, surface_pred):
"""
Given the surface pred, return the original annotated verb
"""
# Assumes that at this point the verb is always the last word
# in the surface predicate
return surface_pred.split(' ')[-1]
def compute_global_pred(self, surface_pred, questions):
"""
Given the surface pred and all instansiations of questions,
make global coherence decisions regarding the final form of the predicate
This should hopefully take care of multi word predicates and correct inflections
"""
from operator import itemgetter
split_surface = surface_pred.split(' ')
if len(split_surface) > 1:
# This predicate has a modal preceding the base verb
verb = split_surface[-1]
ret = split_surface[:-1] # get all of the elements in the modal
else:
verb = split_surface[0]
ret = []
split_questions = map(lambda question: question.split(' '),
questions)
preds = map(normalize_element,
map(itemgetter(QUESTION_TRG_INDEX),
split_questions))
if len(set(preds)) > 1:
# This predicate is appears in multiple ways, let's stick to the base form
ret.append(verb)
if len(set(preds)) == 1:
# Change the predciate to the inflected form
# if there's exactly one way in which the predicate is conveyed
ret.append(preds[0])
pps = map(normalize_element,
map(itemgetter(QUESTION_PP_INDEX),
split_questions))
obj2s = map(normalize_element,
map(itemgetter(QUESTION_OBJ2_INDEX),
split_questions))
if (len(set(pps)) == 1):
# If all questions for the predicate include the same pp attachemnt -
# assume it's a multiword predicate
self.is_mwp = True # Signal to arguments that they shouldn't take the preposition
ret.append(pps[0])
# Concat all elements in the predicate and return
return " ".join(ret).strip()
def augment_arg_with_question(self, arg, question):
"""
Decide what elements from the question to incorporate in the given
corresponding argument
"""
# Parse question
wh, aux, sbj, trg, obj1, pp, obj2 = map(normalize_element,
question.split(' ')[:-1]) # Last split is the question mark
# Place preposition in argument
# This is safer when dealing with n-ary arguments, as it's directly attaches to the
# appropriate argument
if (not self.is_mwp) and pp and (not obj2):
if not(arg.startswith("{} ".format(pp))):
# Avoid repeating the preporition in cases where both question and answer contain it
return " ".join([pp,
arg])
# Normal cases
return arg
def clusterScore(self, cluster):
"""
Calculate cluster density score as the mean distance of the maximum distance of each slot.
Lower score represents a denser cluster.
"""
logging.debug("*-*-*- Cluster: {}".format(cluster))
# Find global centroid
arr = np.array([x for ls in cluster for x in ls])
centroid = np.sum(arr)/arr.shape[0]
logging.debug("Centroid: {}".format(centroid))
# Calculate mean over all maxmimum points
return np.average([max([abs(x - centroid) for x in ls]) for ls in cluster])
def resolveAmbiguity(self):
"""
Heursitic to map the elments (argument and predicates) of this extraction
back to the indices of the sentence.
"""
## TODO: This removes arguments for which there was no consecutive span found
## Part of these are non-consecutive arguments,
## but other could be a bug in recognizing some punctuation marks
elements = [self.pred] \
+ [(s, indices)
for (s, indices)
in self.args
if indices]
logging.debug("Resolving ambiguity in: {}".format(elements))
# Collect all possible combinations of arguments and predicate indices
# (hopefully it's not too much)
all_combinations = list(itertools.product(*map(itemgetter(1), elements)))
logging.debug("Number of combinations: {}".format(len(all_combinations)))
# Choose the ones with best clustering and unfold them
resolved_elements = zip(map(itemgetter(0), elements),
min(all_combinations,
key = lambda cluster: self.clusterScore(cluster)))
logging.debug("Resolved elements = {}".format(resolved_elements))
self.pred = resolved_elements[0]
self.args = resolved_elements[1:]
def conll(self, external_feats = {}):
"""
Return a CoNLL string representation of this extraction
"""
return '\n'.join(["\t".join(map(str,
[i, w] + \
list(self.pred) + \
[self.head_pred_index] + \
external_feats + \
[self.get_label(i)]))
for (i, w)
in enumerate(self.sent.split(" "))]) + '\n'
def get_label(self, index):
"""
Given an index of a word in the sentence -- returns the appropriate BIO conll label
Assumes that ambiguation was already resolved.
"""
# Get the element(s) in which this index appears
ent = [(elem_ind, elem)
for (elem_ind, elem)
in enumerate(map(itemgetter(1),
[self.pred] + self.args))
if index in elem]
if not ent:
# index doesnt appear in any element
return "O"
if len(ent) > 1:
# The same word appears in two different answers
# In this case we choose the first one as label
logging.warn("Index {} appears in one than more element: {}".\
format(index,
"\t".join(map(str,
[ent,
self.sent,
self.pred,
self.args]))))
## Some indices appear in more than one argument (ones where the above message appears)
## From empricial observation, these seem to mostly consist of different levels of granularity:
## what had _ been taken _ _ _ ? loan commitments topping $ 3 billion
## how much had _ been taken _ _ _ ? topping $ 3 billion
## In these cases we heuristically choose the shorter answer span, hopefully creating minimal spans
## E.g., in this example two arguemnts are created: (loan commitments, topping $ 3 billion)
elem_ind, elem = min(ent, key = lambda _, ls: len(ls))
# Distinguish between predicate and arguments
prefix = "P" if elem_ind == 0 else "A{}".format(elem_ind - 1)
# Distinguish between Beginning and Inside labels
suffix = "B" if index == elem[0] else "I"
return "{}-{}".format(prefix, suffix)
def __str__(self):
return '{0}\t{1}'.format(self.elementToStr(self.pred,
print_indices = True),
'\t'.join([self.elementToStr(arg)
for arg
in self.args]))
# Flatten a list of lists
flatten = lambda l: [item for sublist in l for item in sublist]
def normalize_element(elem):
"""
Return a surface form of the given question element.
the output should be properly able to precede a predicate (or blank otherwise)
"""
return elem.replace("_", " ") \
if (elem != "_")\
else ""
## Helper functions
def escape_special_chars(s):
return s.replace('\t', '\\t')
def generalize_question(question):
"""
Given a question in the context of the sentence and the predicate index within
the question - return a generalized version which extracts only order-imposing features
"""
import nltk # Using nltk since couldn't get spaCy to agree on the tokenization
wh, aux, sbj, trg, obj1, pp, obj2 = question.split(' ')[:-1] # Last split is the question mark
return ' '.join([wh, sbj, obj1])
## CONSTANTS
SEP = ';;;'
QUESTION_TRG_INDEX = 3 # index of the predicate within the question
QUESTION_PP_INDEX = 5
QUESTION_OBJ2_INDEX = 6
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