| import os |
|
|
| import datasets |
| import numpy as np |
| import pandas as pd |
|
|
| from .bigbiohub import text_features |
| from .bigbiohub import kb_features |
| from .bigbiohub import BigBioConfig |
| from .bigbiohub import Tasks |
|
|
| _LANGUAGES = ['French'] |
| _PUBMED = False |
| _LOCAL = True |
| _CITATION = """\ |
| @misc{dalloux, title={Datasets – Clément Dalloux}, url={http://clementdalloux.fr/?page_id=28}, journal={Clément Dalloux}, author={Dalloux, Clément}} |
| """ |
|
|
| _DATASETNAME = "essai" |
| _DISPLAYNAME = "ESSAI" |
|
|
| _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 ESSAI corpus |
| """ |
|
|
| _HOMEPAGE = "https://clementdalloux.fr/?page_id=28" |
|
|
| _LICENSE = 'Data User Agreement' |
|
|
| _URLS = { |
| "essai_source": "", |
| "essai_bigbio_text": "", |
| "essai_bigbio_kb": "", |
| } |
|
|
| _SOURCE_VERSION = "1.0.0" |
| _BIGBIO_VERSION = "1.0.0" |
|
|
| _SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION] |
|
|
|
|
| class ESSAI(datasets.GeneratorBasedBuilder): |
| SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
| BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) |
|
|
| DEFAULT_CONFIG_NAME = "essai_source" |
|
|
| BUILDER_CONFIGS = [ |
| BigBioConfig( |
| name="essai_source", |
| version=SOURCE_VERSION, |
| description="ESSAI source schema", |
| schema="source", |
| subset_id="essai", |
| ), |
| BigBioConfig( |
| name="essai_bigbio_text", |
| version=BIGBIO_VERSION, |
| description="ESSAI simplified BigBio schema for negation/speculation classification", |
| schema="bigbio_text", |
| subset_id="essai", |
| ), |
| BigBioConfig( |
| name="essai_bigbio_kb", |
| version=BIGBIO_VERSION, |
| description="ESSAI simplified BigBio schema for part-of-speech-tagging", |
| schema="bigbio_kb", |
| subset_id="essai", |
| ), |
| ] |
|
|
| def _info(self): |
| if self.config.schema == "source": |
| features = datasets.Features( |
| { |
| "id": datasets.Value("string"), |
| "document_id": datasets.Value("string"), |
| "text": [datasets.Value("string")], |
| "lemmas": [datasets.Value("string")], |
| "POS_tags": [datasets.Value("string")], |
| "labels": [datasets.Value("string")], |
| } |
| ) |
| elif self.config.schema == "bigbio_text": |
| features = text_features |
| elif self.config.schema == "bigbio_kb": |
| features = kb_features |
|
|
| 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}, |
| ), |
| ] |
|
|
| def _generate_examples(self, datadir): |
| key = 0 |
| |
| filepath = os.path.join(datadir, "ESSAI_neg.txt") |
|
|
| filepath2 = os.path.join(datadir, 'ESSAI_spec.txt') |
| |
| id_docs = [] |
| id_docs_2 = [] |
| id_words = [] |
| words = [] |
| lemmas = [] |
| POS_tags = [] |
| NER_tags = [] |
| NER_tags_2 = [] |
|
|
| with open(filepath) as f: |
| for line in f.readlines(): |
| line_content = line.split("\t") |
| if len(line_content) > 1: |
| id_docs.append(line_content[0]) |
| id_words.append(line_content[1]) |
| words.append(line_content[2]) |
| lemmas.append(line_content[3]) |
| POS_tags.append(line_content[4]) |
| NER_tags.append(line_content[5].strip()) |
| |
| with open(filepath2) as f: |
| for line in f.readlines(): |
| line_content = line.split("\t") |
| if len(line_content) > 1: |
| id_docs_2.append(line_content[0]) |
| NER_tags_2.append(line_content[5].strip()) |
|
|
| dic = { |
| "id_docs": np.array(list(map(int, id_docs))), |
| "id_words": id_words, |
| "words": words, |
| "lemmas": lemmas, |
| "POS_tags": POS_tags, |
| "NER_tags": NER_tags |
| } |
| dic2 = { |
| "id_docs": np.array(list(map(int, id_docs_2))), |
| "NER_tags": NER_tags_2 |
| } |
| if self.config.schema == "source": |
| for doc_id in set(dic["id_docs"]): |
| idces = np.argwhere(dic["id_docs"] == doc_id)[:, 0] |
| text = [dic["words"][id] for id in idces] |
| text_lemmas = [dic["lemmas"][id] for id in idces] |
| POS_tags_ = [dic["POS_tags"][id] for id in idces] |
| yield key, { |
| "id": key, |
| "document_id": doc_id, |
| "text": text, |
| "lemmas": text_lemmas, |
| "POS_tags": POS_tags_, |
| "labels": [], |
| } |
| key += 1 |
| elif self.config.schema == "bigbio_text": |
| for doc_id in set(dic["id_docs"]): |
| idces = np.argwhere(dic["id_docs"] == doc_id)[:, 0] |
| idces_2 = np.argwhere(dic2["id_docs"] == doc_id)[:, 0] |
| |
| text = " ".join([dic["words"][id] for id in idces]) |
| label_tokens = [dic["NER_tags"][id] for id in idces] |
| label2_tokens = [dic2["NER_tags"][id] for id in idces_2] |
| label_ = [] |
| if not all(l == '***' for l in label_tokens): |
| label_.append("negation") |
| if not all(l == '***' for l in label2_tokens): |
| label_.append("speculation") |
| yield key, { |
| "id": key, |
| "document_id": doc_id, |
| "text": text, |
| "labels": label_, |
| } |
| key += 1 |
| elif self.config.schema == "bigbio_kb": |
| for doc_id in set(dic["id_docs"]): |
| idces = np.argwhere(dic["id_docs"] == doc_id)[:, 0] |
| text = [dic["words"][id] for id in idces] |
| POS_tags_ = [dic["POS_tags"][id] for id in idces] |
|
|
| data = { |
| "id": str(key), |
| "document_id": doc_id, |
| "passages": [], |
| "entities": [], |
| "relations": [], |
| "events": [], |
| "coreferences": [], |
| } |
| key += 1 |
|
|
| data["passages"] = [ |
| { |
| "id": str(key + i), |
| "type": "sentence", |
| "text": [text[i]], |
| "offsets": [[i, i + 1]], |
| } |
| for i in range(len(text)) |
| ] |
| key += len(text) |
|
|
| for i in range(len(text)): |
| entity = { |
| "id": key, |
| "type": "POS_tag", |
| "text": [POS_tags_[i]], |
| "offsets": [[i, i + 1]], |
| "normalized": [], |
| } |
| data["entities"].append(entity) |
| key += 1 |
|
|
| yield key, data |
|
|
