Update CAS.py
Browse files
CAS.py
CHANGED
@@ -61,28 +61,67 @@ _LICENSE = 'Data User Agreement'
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class CAS(datasets.GeneratorBasedBuilder):
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DEFAULT_CONFIG_NAME = "
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="
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]
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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@@ -131,55 +170,201 @@ class CAS(datasets.GeneratorBasedBuilder):
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key = 0
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id_docs = []
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id_words = []
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words = []
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lemmas = []
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POS_tags = []
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continue
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"id": str(key),
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"document_id": str(doc_id),
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"tokens": tokens,
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"lemmas": text_lemmas,
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"pos_tags": pos_tags,
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"label": label,
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})
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key += 1
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ids = [r["id"] for r in all_res]
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class CAS(datasets.GeneratorBasedBuilder):
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DEFAULT_CONFIG_NAME = "pos_spec"
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="pos", version="1.0.0", description="The CAS corpora - POS Speculation task"),
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datasets.BuilderConfig(name="cls", version="1.0.0", description="The CAS corpora - CLS Negation / Speculation task"),
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datasets.BuilderConfig(name="ner_spec", version="1.0.0", description="The CAS corpora - NER Speculation task"),
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datasets.BuilderConfig(name="ner_neg", version="1.0.0", description="The CAS corpora - NER Negation task"),
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]
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def _info(self):
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if self.config.name.find("pos") != -1:
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features = datasets.Features(
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{
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"id": datasets.Value("string"),
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"document_id": datasets.Value("string"),
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"tokens": [datasets.Value("string")],
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"lemmas": [datasets.Value("string")],
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# "pos_tags": [datasets.Value("string")],
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"pos_tags": [datasets.features.ClassLabel(
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names = ['INT', 'PRO:DEM', 'VER:impf', 'VER:ppre', 'PRP:det', 'KON', 'VER:pper', 'PRP', 'PRO:IND', 'VER:simp', 'VER:con', 'SENT', 'VER:futu', 'PRO:PER', 'VER:infi', 'ADJ', 'NAM', 'NUM', 'PUN:cit', 'PRO:REL', 'VER:subi', 'ABR', 'NOM', 'VER:pres', 'DET:ART', 'VER:cond', 'VER:subp', 'DET:POS', 'ADV', 'SYM', 'PUN'],
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)],
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}
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)
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elif self.config.name.find("cls") != -1:
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features = datasets.Features(
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{
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"id": datasets.Value("string"),
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"document_id": datasets.Value("string"),
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"tokens": [datasets.Value("string")],
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# "label": datasets.Value("string"),
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"label": datasets.features.ClassLabel(
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names = ['negation_speculation', 'speculation', 'neutral', 'negation'],
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),
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}
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)
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elif self.config.name.find("ner") != -1:
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if self.config.name.find("_spec") != -1:
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names = ['O', 'B_xcope_inc', 'I_xcope_inc']
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elif self.config.name.find("_neg") != -1:
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names = ['O', 'B_scope_neg', 'I_scope_neg']
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features = datasets.Features(
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{
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"id": datasets.Value("string"),
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"document_id": datasets.Value("string"),
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"tokens": [datasets.Value("string")],
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"lemmas": [datasets.Value("string")],
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# "ner_tags": [datasets.Value("string")],
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"ner_tags": [datasets.features.ClassLabel(
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names = names,
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)],
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}
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)
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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key = 0
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subset = self.config.name.split("_")[-1]
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unique_id_doc = []
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if self.config.name.find("ner") != -1:
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docs = [f"CAS_{subset}.txt"]
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else:
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docs = ["CAS_neg.txt", "CAS_spec.txt"]
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for file in docs:
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filename = os.path.join(datadir, file)
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if self.config.name.find("pos") != -1:
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id_docs = []
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id_words = []
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words = []
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lemmas = []
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POS_tags = []
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with open(filename) as f:
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for line in f.readlines():
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splitted = line.split("\t")
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if len(splitted) < 5:
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continue
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id_doc, id_word, word, lemma, tag = splitted[0:5]
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if len(splitted) >= 8:
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tag = splitted[6]
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if tag == "@card@":
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print(splitted)
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if word == "@card@":
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print(splitted)
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if lemma == "000" and tag == "@card@":
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tag = "NUM"
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word = "100 000"
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lemma = "100 000"
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elif lemma == "45" and tag == "@card@":
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tag = "NUM"
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# if id_doc in id_docs:
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# continue
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id_docs.append(id_doc)
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id_words.append(id_word)
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words.append(word)
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lemmas.append(lemma)
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POS_tags.append(tag)
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dic = {
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"id_docs": np.array(list(map(int, id_docs))),
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"id_words": id_words,
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"words": words,
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"lemmas": lemmas,
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"POS_tags": POS_tags,
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}
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for doc_id in set(dic["id_docs"]):
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indexes = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
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tokens = [dic["words"][id] for id in indexes]
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text_lemmas = [dic["lemmas"][id] for id in indexes]
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pos_tags = [dic["POS_tags"][id] for id in indexes]
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if doc_id not in unique_id_doc:
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all_res.append({
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"id": str(doc_id),
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"document_id": doc_id,
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"tokens": tokens,
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"lemmas": text_lemmas,
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"pos_tags": pos_tags,
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})
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unique_id_doc.append(doc_id)
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# key += 1
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elif self.config.name.find("ner") != -1:
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id_docs = []
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id_words = []
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words = []
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lemmas = []
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ner_tags = []
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with open(filename) as f:
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for line in f.readlines():
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if len(line.split("\t")) < 5:
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continue
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id_doc, id_word, word, lemma, _ = line.split("\t")[0:5]
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tag = line.replace("\n","").split("\t")[-1]
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if tag == "***" or tag == "_":
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tag = "O"
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elif tag == "I_xcope_inc_":
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tag = "I_xcope_inc"
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# elif tag == "v":
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# tag = "I_scope_spec"
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# elif tag == "z":
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# tag = "O"
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id_docs.append(id_doc)
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id_words.append(id_word)
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words.append(word)
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lemmas.append(lemma)
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ner_tags.append(tag)
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dic = {
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"id_docs": np.array(list(map(int, id_docs))),
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"id_words": id_words,
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"words": words,
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"lemmas": lemmas,
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"ner_tags": ner_tags,
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}
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for doc_id in set(dic["id_docs"]):
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indexes = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
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tokens = [dic["words"][id] for id in indexes]
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text_lemmas = [dic["lemmas"][id] for id in indexes]
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ner_tags = [dic["ner_tags"][id] for id in indexes]
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all_res.append({
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"id": key,
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"document_id": doc_id,
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"tokens": tokens,
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"lemmas": text_lemmas,
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"ner_tags": ner_tags,
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})
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key += 1
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elif self.config.name.find("cls") != -1:
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f_in = open(filename, "r")
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conll = [
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[b.split("\t") for b in a.split("\n")]
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for a in f_in.read().split("\n\n")
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]
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f_in.close()
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classe = "negation" if filename.find("_neg") != -1 else "speculation"
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for document in conll:
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if document == [""]:
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continue
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identifier = document[0][0]
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unique = list(set([w[-1] for w in document]))
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tokens = [sent[2] for sent in document if len(sent) > 1]
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if "***" in unique:
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l = "neutral"
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elif "_" in unique:
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l = classe
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if identifier in unique_id_doc and l == 'neutral':
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continue
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elif identifier in unique_id_doc and l != 'neutral':
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index_l = unique_id_doc.index(identifier)
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if all_res[index_l]["label"] != "neutral":
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l = "negation_speculation"
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all_res[index_l] = {
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"id": str(identifier),
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"document_id": identifier,
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"tokens": tokens,
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"label": l,
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}
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else:
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all_res.append({
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"id": str(identifier),
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"document_id": identifier,
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"tokens": tokens,
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"label": l,
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})
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unique_id_doc.append(identifier)
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ids = [r["id"] for r in all_res]
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