Create CAS.py

CAS.py

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+import os
+import random
+
+import datasets
+import numpy as np
+
+_CITATION = """\
+@inproceedings{grabar-etal-2018-cas,
+  title        = {{CAS}: {F}rench Corpus with Clinical Cases},
+  author       = {Grabar, Natalia  and Claveau, Vincent  and Dalloux, Cl{\'e}ment},
+  year         = 2018,
+  month        = oct,
+  booktitle    = {
+    Proceedings of the Ninth International Workshop on Health Text Mining and
+    Information Analysis
+  },
+  publisher    = {Association for Computational Linguistics},
+  address      = {Brussels, Belgium},
+  pages        = {122--128},
+  doi          = {10.18653/v1/W18-5614},
+  url          = {https://aclanthology.org/W18-5614},
+  abstract     = {
+    Textual corpora are extremely important for various NLP applications as
+    they provide information necessary for creating, setting and testing these
+    applications and the corresponding tools. They are also crucial for
+    designing reliable methods and reproducible results. Yet, in some areas,
+    such as the medical area, due to confidentiality or to ethical reasons, it
+    is complicated and even impossible to access textual data representative of
+    those produced in these areas. We propose the CAS corpus built with
+    clinical cases, such as they are reported in the published scientific
+    literature in French. We describe this corpus, currently containing over
+    397,000 word occurrences, and the existing linguistic and semantic
+    annotations.
+  }
+}
+"""
+
+_DESCRIPTION = """\
+We manually annotated two corpora from the biomedical field. The ESSAI corpus \
+contains clinical trial protocols in French. They were mainly obtained from the \
+National Cancer Institute The typical protocol consists of two parts: the \
+summary of the trial, which indicates the purpose of the trial and the methods \
+applied; and a detailed description of the trial with the inclusion and \
+exclusion criteria. The CAS corpus contains clinical cases published in \
+scientific literature and training material. They are published in different \
+journals from French-speaking countries (France, Belgium, Switzerland, Canada, \
+African countries, tropical countries) and are related to various medical \
+specialties (cardiology, urology, oncology, obstetrics, pulmonology, \
+gastro-enterology). The purpose of clinical cases is to describe clinical \
+situations of patients. Hence, their content is close to the content of clinical \
+narratives (description of diagnoses, treatments or procedures, evolution, \
+family history, expected audience, etc.). In clinical cases, the negation is \
+frequently used for describing the patient signs, symptoms, and diagnosis. \
+Speculation is present as well but less frequently.
+This version only contain the annotated CAS corpus
+"""
+
+_HOMEPAGE = "https://clementdalloux.fr/?page_id=28"
+
+_LICENSE = 'Data User Agreement'
+
+class CAS(datasets.GeneratorBasedBuilder):
+
+    DEFAULT_CONFIG_NAME = "source"
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="source", version="1.0.0", description="The CAS corpora"),
+    ]
+
+    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:ppre', 'VER:infi', 'VER:impf', 'VER:simp', 'PUN', 'DET:POS', 'ADV', 'DET:ART', 'PRO:DEM', 'INT', 'VER:futu', 'VER:subp', 'VER:cond', 'VER:pper', 'KON', 'NAM', 'PRO:IND', 'VER:con', 'PRP', 'SYM', 'SENT', 'PUN:cit', 'VER:pres', 'PRP:det', 'PRO:REL', 'PRO:PER', 'VER:subi', 'ADJ', 'NUM', 'NOM', 'ABR'],
+                )],
+                "label": datasets.features.ClassLabel(
+                    names = ['negation', 'speculation'],
+                ),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+
+        if self.config.data_dir is None:
+            raise ValueError("This is a local dataset. Please pass the data_dir kwarg to load_dataset.")
+        
+        else:
+            data_dir = self.config.data_dir
+            
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "datadir": data_dir,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "datadir": data_dir,
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "datadir": data_dir,
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, datadir, split):
+
+        all_res = []
+
+        key = 0
+
+        for file in ["CAS_neg.txt", "CAS_spec.txt"]:
+
+            label = "negation" if "neg" in file else "speculation"
+            id_docs = []
+            id_words = []
+            words = []
+            lemmas = []
+            POS_tags = []
+
+            with open(os.path.join(datadir, file)) as f:
+
+                for line in f.readlines():
+
+                    if len(line.split("\t")) < 5:
+                        continue
+
+                    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": str(key),
+                    "document_id": str(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)
+        random.shuffle(ids)
+        random.shuffle(ids)
+        random.shuffle(ids)
+
+        train, validation, test = np.split(ids, [int(len(ids)*0.70), int(len(ids)*0.80)])
+
+        if split == "train":
+            allowed_ids = list(train)
+        elif split == "validation":
+            allowed_ids = list(validation)
+        elif split == "test":
+            allowed_ids = list(test)
+
+        for r in all_res:
+            if r["id"] in allowed_ids:
+                yield r["id"], r