def create_data_set(three_classes, i):
f = []
doc = read_input(i)
sentences = [sent.string.strip() for sent in doc.sents]
print(sentences)
for s in sentences:
features = {}
sen = nlp(s)
features["text"] = s
features["pos"] = pos.get_pos_tags(sen)
features["dep"] = dependency.get_dep_tags(sen)
features["ner"] = ner.get_ner(sen)
features["closing_question_mark"] = sentence_closes_with_question_mark.sentence_closes_with_question_marks(sen) # bool
features["contains_adverb"] = contains_adverb.contains_adverb(sen) # bool
features["contains_modal"] = contains_modal_verb.contains_modal_verb(sen) # bool
features["first_person"] = reference_to_first_person.contains_first_person(sen) # bool
features["argumentative_discourse_markers"] = argumentative_discourse_markers.contains_argumentative_markers(sen)
f.append(features)
# print(features)
# print(f)
dataset = create_dataframe(f)
return dataset
def create_data_set(i):
f = []
f_in = open("../Corpus/%s.txt" % i, "r")
text = f_in.read()
doc = nlp(text)
sentences = [sent.string.strip() for sent in doc.sents]
for s in sentences:
print(s)
features = {}
sen = nlp(s)
features["text"] = s
#features["pos"] = pos.get_pos_tags(sen)
#features["dep"] = dependency.get_dep_tags(sen)
#features["ner"] = ner.get_ner(sen)
features[
"closing_question_mark"] = sentence_closes_with_question_mark.sentence_closes_with_question_marks(
sen) # bool
features["contains_adverb"] = contains_adverb.contains_adverb(
sen) # bool
features["contains_modal"] = contains_modal_verb.contains_modal_verb(
sen) # bool
features[
"first_person"] = reference_to_first_person.contains_first_person(
sen) # bool
#features["causal_markers"] = argumentative_discourse_markers.contains_causal_markers(sen) # bool
#features["conditional_markers"] = argumentative_discourse_markers.contains_conditional_markers(sen) # bool
#features["adversative_markers"] = argumentative_discourse_markers.contains_adversative_markers(sen) # bool
#features["consecutive_markers"] = argumentative_discourse_markers.contains_consecutive_markers(sen) # bool
#features["concessive_markers"] = argumentative_discourse_markers.contains_concessive_markers(sen) # bool
features[
"argumentative_discourse_markers"] = argumentative_discourse_markers.contains_argumentative_markers(
sen)
features["contains_named_entities"] = ner.contains_ner(sen)
features["sentence_length"] = sentence_length.get_sentence_length(sen)
f.append(features)
#print(features)
#print(f)
df = pd.DataFrame.from_dict(f)
X1 = cv.transform(df.text)
# print(X1.toarray())
# print(cv.get_feature_names())
df = df.drop(columns='text')
count_vect_df = pd.DataFrame(X1.todense(), columns=cv.get_feature_names())
# print(pd.concat([df, count_vect_df], axis=1))
combined_df = pd.concat([df, count_vect_df], axis=1)
return combined_df
def create_data_set(text, cv):
f = []
# read text
#f_in = open("../Corpus/%s.txt" % i, "r")
#text = f_in.read()
doc = nlp(text)
sentences = [sent.string.strip() for sent in doc.sents]
# features
for s in sentences:
#print(s)
features = {}
sen = nlp(s)
features["text"] = s
features[
"closing_question_mark"] = sentence_closes_with_question_mark.sentence_closes_with_question_marks(
sen) # bool
features["contains_adverb"] = contains_adverb.contains_adverb(
sen) # bool
features["contains_modal"] = contains_modal_verb.contains_modal_verb(
sen) # bool
features[
"first_person"] = reference_to_first_person.contains_first_person(
sen) # bool
features[
"argumentative_discourse_markers"] = argumentative_discourse_markers.contains_argumentative_markers(
sen)
features["contains_named_entities"] = ner.contains_ner(sen)
features["sentence_length"] = sentence_length.get_sentence_length(sen)
f.append(features)
df = pd.DataFrame.from_dict(f)
X1 = cv.transform(df.text)
df = df.drop(columns='text')
count_vect_df = pd.DataFrame(X1.todense(), columns=cv.get_feature_names())
combined_df = pd.concat([df, count_vect_df], axis=1)
return combined_df
def create_data_set(three_classes):
f = []
for i in range(0, 990):
f_in = open("../Corpus/%s.txt" % i, "r")
text = f_in.read()
f_ann = open("../Corpus/%s.ann" % i, "r")
line = f_ann.readline()
annotations = []
while line:
l = line.split("\t")
if l[0].startswith("T"):
annotation = l[2]
label = l[1].split(" ")[0]
if not three_classes:
if label in ('Claim', 'Premise'):
label = 'Argumentative'
annotations.append((annotation.strip(), label))
line = f_ann.readline()
#for a in annotations:
#print(a)
doc = nlp(text)
sentences = [sent.string.strip() for sent in doc.sents]
for s in sentences:
features = {}
sen = nlp(s)
features["label"] = get_label(s, annotations)
features["text"] = s
#features["pos"] = pos.get_pos_tags(sen)
#features["dep"] = dependency.get_dep_tags(sen)
#features["ner"] = ner.get_ner(sen)
features[
"closing_question_mark"] = sentence_closes_with_question_mark.sentence_closes_with_question_marks(
sen) # bool
features["contains_adverb"] = contains_adverb.contains_adverb(
sen) # bool
features[
"contains_modal"] = contains_modal_verb.contains_modal_verb(
sen) # bool
features[
"first_person"] = reference_to_first_person.contains_first_person(
sen) # bool
#features["causal_markers"] = argumentative_discourse_markers.contains_causal_markers(sen) # bool
#features["conditional_markers"] = argumentative_discourse_markers.contains_conditional_markers(sen) # bool
#features["adversative_markers"] = argumentative_discourse_markers.contains_adversative_markers(sen) # bool
#features["consecutive_markers"] = argumentative_discourse_markers.contains_consecutive_markers(sen) # bool
#features["concessive_markers"] = argumentative_discourse_markers.contains_concessive_markers(sen) # bool
features[
"argumentative_discourse_markers"] = argumentative_discourse_markers.contains_argumentative_markers(
sen)
features["contains_named_entities"] = ner.contains_ner(sen)
features["sentence_length"] = sentence_length.get_sentence_length(
sen)
f.append(features)
#print(features)
#print(f)
dataset = create_dataframe(f)
return dataset
