Update ESSAI.py

ESSAI.py

@@ -41,28 +41,63 @@ _LICENSE = 'Data User Agreement'
 
 class ESSAI(datasets.GeneratorBasedBuilder):
 
-    DEFAULT_CONFIG_NAME = "source"
+    DEFAULT_CONFIG_NAME = "pos_spec"
 
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="source", version="1.0.0", description="The ESSAI corpora"),
+        datasets.BuilderConfig(name="pos", version="1.0.0", description="The ESSAI corpora - POS Speculation task"),
+
+        datasets.BuilderConfig(name="cls_spec", version="1.0.0", description="The ESSAI corpora - CLS Speculation task"),
+        datasets.BuilderConfig(name="cls_neg", version="1.0.0", description="The ESSAI corpora - CLS Negation task"),
+
+        datasets.BuilderConfig(name="ner_spec", version="1.0.0", description="The ESSAI corpora - NER Speculation task"),
+        datasets.BuilderConfig(name="ner_neg", version="1.0.0", description="The ESSAI corpora - NER Negation task"),
     ]
 
     def _info(self):
 
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "document_id": datasets.Value("string"),
-                "tokens": [datasets.Value("string")],
-                "lemmas": [datasets.Value("string")],
-                "pos_tags": [datasets.features.ClassLabel(
-                    names = ['VER:pper', 'VER:subi', 'VER:cond', 'INT', 'VER:infi', 'PUN:cit', 'ITAC', 'PUN', 'VER:ppre', 'VER:pres', 'PRO:REL', 'ADJ', 'VER:subp', 'NN', 'PREF', 'PRP', 'PRO:IND', 'PRO:POS', 'DET:POS', 'VER:futu', 'PRO:DEM', 'KON', 'DET:ART', 'VER:', 'PRP:det', 'PRO', 'FAG', 'NOM', 'SYM', 'VER:impf', 'CIT02-HM', 'SENT', 'Bayer', 'VER:simp', 'ADV', 'bayer', '@card@', 'PRO:PER', 'NUM', 'ABR', 'NAM'],
-                )],
-                "label": datasets.features.ClassLabel(
-                    names = ['negation', 'speculation'],
-                ),
-            }
-        )
+        if self.config.name.find("pos") != -1:
+
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "document_id": datasets.Value("string"),
+                    "tokens": [datasets.Value("string")],
+                    "lemmas": [datasets.Value("string")],
+                    "pos_tags": [datasets.Value("string")],
+                    # "pos_tags": [datasets.features.ClassLabel(
+                    #     names = ['VER:pper', 'VER:subi', 'VER:cond', 'INT', 'VER:infi', 'PUN:cit', 'ITAC', 'PUN', 'VER:ppre', 'VER:pres', 'PRO:REL', 'ADJ', 'VER:subp', 'NN', 'PREF', 'PRP', 'PRO:IND', 'PRO:POS', 'DET:POS', 'VER:futu', 'PRO:DEM', 'KON', 'DET:ART', 'VER:', 'PRP:det', 'PRO', 'FAG', 'NOM', 'SYM', 'VER:impf', 'CIT02-HM', 'SENT', 'Bayer', 'VER:simp', 'ADV', 'bayer', '@card@', 'PRO:PER', 'NUM', 'ABR', 'NAM'],
+                    # )],
+                }
+            )
+        
+        elif self.config.name.find("cls") != -1:
+
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "document_id": datasets.Value("string"),
+                    "tokens": [datasets.Value("string")],
+                    "label": datasets.Value("string"),
+                    # "label": datasets.features.ClassLabel(
+                    #     names = ['VER:pper', 'VER:subi', 'VER:cond', 'INT', 'VER:infi', 'PUN:cit', 'ITAC', 'PUN', 'VER:ppre', 'VER:pres', 'PRO:REL', 'ADJ', 'VER:subp', 'NN', 'PREF', 'PRP', 'PRO:IND', 'PRO:POS', 'DET:POS', 'VER:futu', 'PRO:DEM', 'KON', 'DET:ART', 'VER:', 'PRP:det', 'PRO', 'FAG', 'NOM', 'SYM', 'VER:impf', 'CIT02-HM', 'SENT', 'Bayer', 'VER:simp', 'ADV', 'bayer', '@card@', 'PRO:PER', 'NUM', 'ABR', 'NAM'],
+                    # ),
+                }
+            )
+        
+        elif self.config.name.find("ner") != -1:
+
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "document_id": datasets.Value("string"),
+                    "tokens": [datasets.Value("string")],
+                    "lemmas": [datasets.Value("string")],
+                    "ner_tags": [datasets.Value("string")],
+                    # "ner_tags": [datasets.features.ClassLabel(
+                    #     names = ['VER:pper', 'VER:subi', 'VER:cond', 'INT', 'VER:infi', 'PUN:cit', 'ITAC', 'PUN', 'VER:ppre', 'VER:pres', 'PRO:REL', 'ADJ', 'VER:subp', 'NN', 'PREF', 'PRP', 'PRO:IND', 'PRO:POS', 'DET:POS', 'VER:futu', 'PRO:DEM', 'KON', 'DET:ART', 'VER:', 'PRP:det', 'PRO', 'FAG', 'NOM', 'SYM', 'VER:impf', 'CIT02-HM', 'SENT', 'Bayer', 'VER:simp', 'ADV', 'bayer', '@card@', 'PRO:PER', 'NUM', 'ABR', 'NAM'],
+                    # )],
+                }
+            )
 
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
@@ -111,56 +146,178 @@ class ESSAI(datasets.GeneratorBasedBuilder):
 
         key = 0
 
-        for file in ["ESSAI_neg.txt", "ESSAI_spec.txt"]:
+        subset = self.config.name.split("_")[-1]
 
-            label = "negation" if "neg" in file else "speculation"
-            id_docs = []
-            id_words = []
-            words = []
-            lemmas = []
-            POS_tags = []
+        unique_id_doc = []
 
-            with open(os.path.join(datadir, file)) as f:
+        if self.config.name.find("pos") != -1:
+            docs = ["ESSAI_neg.txt", "ESSAI_spec.txt"]
+        else:
+            docs = [f"ESSAI_{subset}.txt"]
+
+        for file in docs:
+
+            filename = os.path.join(datadir, file)
+
+            if self.config.name.find("pos") != -1:
+
+                id_docs = []
+                id_words = []
+                words = []
+                lemmas = []
+                POS_tags = []
+
+                with open(filename) as f:
+
+                    for line in f.readlines():
+
+                        splitted = line.split("\t")
+
+                        if len(splitted) < 5:
+                            continue
+
+                        id_doc, id_word, word, lemma, tag = splitted[0:5]
+                        if len(splitted) >= 8:
+                            tag = splitted[6]
+                        
+                        if tag == "@card@":
+                            print(splitted)
+                        
+                        if lemma == "000" and tag == "@card@":
+                            tag = "NUM"
+                            word = "100 000"
+                            lemma = "100 000"
+                        elif lemma == "45" and tag == "@card@":
+                            tag = "NUM"
+
+                        # if id_doc in id_docs:
+                        #     continue
+
+                        id_docs.append(id_doc)
+                        id_words.append(id_word)
+                        words.append(word)
+                        lemmas.append(lemma)
+                        POS_tags.append(tag)
+
+                dic = {
+                    "id_docs":  np.array(list(map(int, id_docs))),
+                    "id_words": id_words,
+                    "words":    words,
+                    "lemmas":   lemmas,
+                    "POS_tags": POS_tags,
+                }
+
+                for doc_id in set(dic["id_docs"]):
+
+                    indexes = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
+                    tokens = [dic["words"][id] for id in indexes]
+                    text_lemmas = [dic["lemmas"][id] for id in indexes]
+                    pos_tags = [dic["POS_tags"][id] for id in indexes]
+
+                    if doc_id not in unique_id_doc:
+
+                        all_res.append({
+                            "id": str(doc_id),
+                            "document_id": doc_id,
+                            "tokens": tokens,
+                            "lemmas": text_lemmas,
+                            "pos_tags": pos_tags,
+                        })
+                        unique_id_doc.append(doc_id)
+
+                        # key += 1
+
+            elif self.config.name.find("ner") != -1:
+
+                id_docs = []
+                id_words = []
+                words = []
+                lemmas = []
+                ner_tags = []
+
+                with open(filename) as f:
+
+                    for line in f.readlines():
+
+                        if len(line.split("\t")) < 5:
+                            continue
+
+                        id_doc, id_word, word, lemma, _ = line.split("\t")[0:5]
+                        tag = line.replace("\n","").split("\t")[-1]
+                        if tag == "***" or tag == "_":
+                            tag = "O"
+                        elif tag == "v":
+                            tag = "I_scope_spec"
+                        elif tag == "z":
+                            tag = "O"
+
+                        id_docs.append(id_doc)
+                        id_words.append(id_word)
+                        words.append(word)
+                        lemmas.append(lemma)
+                        ner_tags.append(tag)
+
+                dic = {
+                    "id_docs":  np.array(list(map(int, id_docs))),
+                    "id_words": id_words,
+                    "words":    words,
+                    "lemmas":   lemmas,
+                    "ner_tags": ner_tags,
+                }
+
+                for doc_id in set(dic["id_docs"]):
+
+                    indexes = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
+                    tokens = [dic["words"][id] for id in indexes]
+                    text_lemmas = [dic["lemmas"][id] for id in indexes]
+                    ner_tags = [dic["ner_tags"][id] for id in indexes]
+
+                    all_res.append({
+                        "id": key,
+                        "document_id": doc_id,
+                        "tokens": tokens,
+                        "lemmas": text_lemmas,
+                        "ner_tags": ner_tags,
+                    })
+
+                    key += 1
+
+            elif self.config.name.find("cls") != -1:
+
+                f_in = open(filename, "r")
+                conll = [
+                    [b.split("\t") for b in a.split("\n")]
+                    for a in f_in.read().split("\n\n")
+                ]
+                f_in.close()
+
+                classe = "negation" if self.config.name.find("neg") != -1 else "speculation"
+
+                all_res = []
+
+                for document in conll:
+
+                    if document == [""]:
+                        continue
 
-                for line in f.readlines():
+                    identifier = document[0][0]
 
-                    if len(line.split("\t")) < 5:
-                        continue
+                    unique = list(set([w[-1] for w in document]))
+                    # print(document)
+                    tokens = [sent[2] for sent in document if len(sent) > 1]
+
+                    if "***" in unique:
+                        l = "none"
+                    elif "_" in unique:
+                        l = classe
+
+                    all_res.append({
+                        "id": str(identifier),
+                        "document_id": identifier,
+                        "tokens": tokens,
+                        "label": l,
+                    })
 
-                    id_doc, id_word, word, lemma, tag = line.split("\t")[0:5]
-
-                    id_docs.append(id_doc)
-                    id_words.append(id_word)
-                    words.append(word)
-                    lemmas.append(lemma)
-                    POS_tags.append(tag)
-
-            dic = {
-                "id_docs":  np.array(list(map(int, id_docs))),
-                "id_words": id_words,
-                "words":    words,
-                "lemmas":   lemmas,
-                "POS_tags": POS_tags,
-            }
-
-            for doc_id in set(dic["id_docs"]):
-
-                indexes = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
-                tokens = [dic["words"][id] for id in indexes]
-                text_lemmas = [dic["lemmas"][id] for id in indexes]
-                pos_tags = [dic["POS_tags"][id] for id in indexes]
-
-                all_res.append({
-                    "id": key,
-                    "document_id": doc_id,
-                    "tokens": tokens,
-                    "lemmas": text_lemmas,
-                    "pos_tags": pos_tags,
-                    "label": label,
-                })
-
-                key += 1
-        
         ids = [r["id"] for r in all_res]
 
         random.seed(4)