def get_hedges_per_sentence(directory):
""""Takes all KAF-files in a directory as input and returns a dictionary with all the sentences that contain a hedge cue, with the sentence id as key and a list of the hedge cues"""
hedge_cues = ["possible", "possibly", "probably", "probable", "might", "may", "possibility", "probability", "presume", "suppose", "suggest", "believe", "think", "if", "perhaps", "maybe", "likely", "could", "speculate", "speculation", "suspect", "guess", "predict", "prediction", "whether", "improbable", "seem", "question", "indicate", "indication", "unsure", "allegedly", "apparently", "favor", "unlikely", "doubt", "assume", "assumption", "certainty", "uncertainty", "certain", 'uncertain', 'questionable', 'ambivalent', 'dubious', 'erratic', 'hazy', 'hesitant', 'insecure', 'unclear', 'undetermined', 'unpredictable', 'predictable', 'unsure', 'speculative', 'indefinite', 'indeterminate', 'doubtful', 'disbelieve', 'potential', 'potentially', 'imaginable', 'fear', 'hope', 'thinkable', 'promising', 'promise', 'hopeful', 'hopefully', 'feasible', 'feasibly', 'reasonably', 'conceivably', 'reasonable', 'conceivable', 'perchance', 'imaginably', 'presumably', 'seemingly', 'assumable', 'expect', 'expectation', 'expectedly', 'consider']
dict_hedges_per_sentence = {}
hedge_terms = {}
hedge_words = {}
hedges_in_sentences = {}
# CHECK WHICH TERMS ARE HEDGE CUES
for filename in listdir(directory):
path = directory + "\\" + filename
try:
root = get_root(path)
except:
print 'Error: could not process', filename
terms_layer = root.find("terms")
for term in terms_layer.findall("term"):
term_id = term.get("tid")
term_id = filename + "_" + term_id
lemma = term.get("lemma")
if lemma in hedge_cues:
hedge_terms[term_id] = lemma
#print hedge_terms
# CHECK WHICH WORDS ARE HEDGE CUES
dict_terms_words = get_terms_words(directory)
for term in dict_terms_words:
if term in hedge_terms:
word_id = dict_terms_words[term]
lemma = hedge_terms[term]
hedge_words[word_id] = lemma
# CHECK WHICH HEDGE CUES EACH SENTENCE CONTAINS AND CREATE DICTIONARY
dict_sentences_words = get_sentences_words(directory)
for sent_id in dict_sentences_words:
span_words = dict_sentences_words[sent_id]
for word_id in span_words:
if word_id in hedge_words:
lemma = hedge_words[word_id]
if sent_id not in hedges_in_sentences:
hedges_in_sentences[sent_id] = [lemma]
else:
hedges_in_sentences[sent_id].append(lemma)
if sent_id not in hedges_in_sentences:
hedges_in_sentences[sent_id] = "No hedge cues"
return hedges_in_sentences
def get_opinionated_sentences(directory):
"""Takes all KAF-files from a directory and returns a set of all the speculative sentences in these files (sentence ids)."""
opinionated_terms = []
opinionated_words = []
opinionated_sentences = []
# CHECK WHICH TERMS ARE SPECULATIVE IN ALL KAF-FILES FROM DIRECTORY: CREATE SPECULATIVE_TERMS LIST
print "Collecting opinionated terms"
for filename in listdir(directory):
path = directory + "\\" + filename
try:
root = get_root(path)
except:
print "Error: could not process", filename
try:
opinions_layer = root.find("opinions")
for opinion in opinions_layer.findall("opinion"):
for expression in opinion.findall("opinion_expression"):
span = expression.find("span")
for target in span.findall("target"):
term = target.get("id")
term = filename + "_" + term
opinionated_terms.append(term)
except:
print "Error: no opinion found in", filename
# CHECK WHICH WORDS ARE SPECULATIVE USING THE TERMS-WORDS DICTIONARY: CREATE SPECULATIVE_WORDS LIST
print "Collecting opinionated words"
dict_terms_words = get_terms_words(directory)
for term in dict_terms_words:
if term in opinionated_terms:
word_id = dict_terms_words[term]
opinionated_words.append(word_id)
# CHECK WHICH SENTENCES ARE SPECULATIVE USING THE SENTENCES-WORDS DICTIONARY: ADD TO SPECULATIVE_SENTENCES SET
print "Collecting opinionated sentences"
dict_sentences_words = get_sentences_words(directory)
for sent_id in dict_sentences_words:
span_words = dict_sentences_words[sent_id]
for word_id in span_words:
if word_id in opinionated_words:
opinionated_sentences.append(sent_id)
opinionated_sentences = set(opinionated_sentences)
return opinionated_sentences