| import os |
|
|
| import datasets |
|
|
| from .bigbiohub import kb_features |
| from .bigbiohub import BigBioConfig |
| from .bigbiohub import Tasks |
|
|
| _LANGUAGES = ['English'] |
| _PUBMED = True |
| _LOCAL = False |
| _CITATION = """\ |
| @article{VANMULLIGEN2012879, |
| title = {The EU-ADR corpus: Annotated drugs, diseases, targets, and their relationships}, |
| journal = {Journal of Biomedical Informatics}, |
| volume = {45}, |
| number = {5}, |
| pages = {879-884}, |
| year = {2012}, |
| note = {Text Mining and Natural Language Processing in Pharmacogenomics}, |
| issn = {1532-0464}, |
| doi = {https://doi.org/10.1016/j.jbi.2012.04.004}, |
| url = {https://www.sciencedirect.com/science/article/pii/S1532046412000573}, |
| author = {Erik M. {van Mulligen} and Annie Fourrier-Reglat and David Gurwitz and Mariam Molokhia and Ainhoa Nieto and Gianluca Trifiro and Jan A. Kors and Laura I. Furlong}, |
| keywords = {Text mining, Corpus development, Machine learning, Adverse drug reactions}, |
| abstract = {Corpora with specific entities and relationships annotated are essential to train and evaluate text-mining systems that are developed to extract specific structured information from a large corpus. In this paper we describe an approach where a named-entity recognition system produces a first annotation and annotators revise this annotation using a web-based interface. The agreement figures achieved show that the inter-annotator agreement is much better than the agreement with the system provided annotations. The corpus has been annotated for drugs, disorders, genes and their inter-relationships. For each of the drug–disorder, drug–target, and target–disorder relations three experts have annotated a set of 100 abstracts. These annotated relationships will be used to train and evaluate text-mining software to capture these relationships in texts.} |
| } |
| """ |
|
|
| _DATASETNAME = "euadr" |
| _DISPLAYNAME = "EU-ADR" |
|
|
| _DESCRIPTION = """\ |
| Corpora with specific entities and relationships annotated are essential to \ |
| train and evaluate text-mining systems that are developed to extract specific \ |
| structured information from a large corpus. In this paper we describe an \ |
| approach where a named-entity recognition system produces a first annotation and \ |
| annotators revise this annotation using a web-based interface. The agreement \ |
| figures achieved show that the inter-annotator agreement is much better than the \ |
| agreement with the system provided annotations. The corpus has been annotated \ |
| for drugs, disorders, genes and their inter-relationships. For each of the \ |
| drug-disorder, drug-target, and target-disorder relations three experts \ |
| have annotated a set of 100 abstracts. These annotated relationships will be \ |
| used to train and evaluate text-mining software to capture these relationships \ |
| in texts. |
| """ |
|
|
| _HOMEPAGE = "https://www.sciencedirect.com/science/article/pii/S1532046412000573" |
|
|
| _LICENSE = 'License information unavailable' |
|
|
| _URL = "https://biosemantics.erasmusmc.nl/downloads/euadr.tgz" |
|
|
| _SOURCE_VERSION = "1.0.0" |
| _BIGBIO_VERSION = "1.0.0" |
|
|
| _SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION] |
|
|
|
|
| class EUADR(datasets.GeneratorBasedBuilder): |
| SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
| BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) |
|
|
| DEFAULT_CONFIG_NAME = "euadr_bigbio_kb" |
|
|
| BUILDER_CONFIGS = [ |
| BigBioConfig( |
| name="euadr_source", |
| version=SOURCE_VERSION, |
| description="EU-ADR source schema", |
| schema="source", |
| subset_id="euadr", |
| ), |
| BigBioConfig( |
| name="euadr_bigbio_kb", |
| version=BIGBIO_VERSION, |
| description="EU-ADR simplified BigBio schema for named entity recognition and relation extraction", |
| schema="bigbio_kb", |
| subset_id="euadr", |
| ), |
| ] |
|
|
| def _info(self): |
| if self.config.schema == "source": |
| features = datasets.Features( |
| { |
| "pmid": datasets.Value("string"), |
| "title": datasets.Value("string"), |
| "abstract": datasets.Value("string"), |
| "annotations": datasets.Sequence(datasets.Value("string")), |
| } |
| ) |
| 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): |
| urls = _URL |
| datapath = dl_manager.download_and_extract(urls) |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={"datapath": datapath, "dl_manager": dl_manager}, |
| ), |
| ] |
|
|
| def _generate_examples(self, datapath, dl_manager): |
| def replace_html_special_chars(string): |
| |
| |
| for (i, r) in [ |
| (""", '"'), |
| (""", '"'), |
| ("'", "'"), |
| ("'", "'"), |
| ("&", "&"), |
| ("&", "&"), |
| ("<", "<"), |
| ("<", "<"), |
| (">", ">"), |
| (">", ">"), |
| ("'", "'"), |
| ]: |
| string = string.replace(i, r) |
| return string |
|
|
| def suppr_blank(l_str): |
| r = [] |
| for string in l_str: |
| if len(string) > 0: |
| r.append(string) |
| return r |
|
|
| folder_path = os.path.join(datapath, "euadr_corpus") |
| key = 0 |
| if self.config.schema == "source": |
| for filename in sorted(os.listdir(folder_path)): |
| if "_" not in filename: |
| corpus_path = dl_manager.download_and_extract( |
| f"https://pubmed.ncbi.nlm.nih.gov/{filename[:-4]}/?format=pubmed" |
| ) |
| with open(corpus_path, "r", encoding="latin") as f: |
| full_html = replace_html_special_chars( |
| ("".join(f.readlines())) |
| .replace("\r\n", "") |
| .replace("\n", "") |
| ) |
| abstract = " ".join( |
| suppr_blank( |
| full_html.split("AB -")[-1] |
| .split("FAU -")[0] |
| .split(" ") |
| ) |
| ) |
| title = " ".join( |
| suppr_blank( |
| full_html.split("TI -")[-1].split("PG")[0].split(" ") |
| ) |
| ) |
| full_text = " ".join([title, abstract]) |
| with open( |
| os.path.join(folder_path, filename), "r", encoding="latin" |
| ) as f: |
| lines = f.readlines() |
| yield key, { |
| "pmid": filename[:-4], |
| "title": title, |
| "abstract": abstract, |
| "annotations": lines, |
| } |
| key += 1 |
| elif self.config.schema == "bigbio_kb": |
| for filename in sorted(os.listdir(folder_path)): |
| if "_" not in filename: |
| corpus_path = dl_manager.download_and_extract( |
| f"https://pubmed.ncbi.nlm.nih.gov/{filename[:-4]}/?format=pubmed" |
| ) |
| with open(corpus_path, "r", encoding="latin") as f: |
| full_html = replace_html_special_chars( |
| ("".join(f.readlines())) |
| .replace("\r\n", "") |
| .replace("\n", "") |
| ) |
| abstract = " ".join( |
| suppr_blank( |
| full_html.split("AB -")[-1] |
| .split("FAU -")[0] |
| .split(" ") |
| ) |
| ) |
| title = " ".join( |
| suppr_blank( |
| full_html.split("TI -")[-1].split("PG")[0].split(" ") |
| ) |
| ) |
| full_text = " ".join([title, abstract]) |
| with open( |
| os.path.join(folder_path, filename), "r", encoding="latin" |
| ) as f: |
| lines = f.readlines() |
| data = { |
| "id": str(key), |
| "document_id": str(key), |
| "passages": [], |
| "entities": [], |
| "events": [], |
| "coreferences": [], |
| "relations": [], |
| } |
| key += 1 |
| data["passages"].append( |
| { |
| "id": str(key), |
| "type": "title", |
| "text": [title], |
| "offsets": [[0, len(title)]], |
| } |
| ) |
| key += 1 |
| data["passages"].append( |
| { |
| "id": str(key), |
| "type": "abstract", |
| "text": [abstract], |
| "offsets": [ |
| [len(title) + 1, len(title) + 1 + len(abstract)] |
| ], |
| } |
| ) |
| key += 1 |
| for line in lines: |
| line_processed = line.split("\t") |
| if line_processed[2] == "relation": |
| data["entities"].append( |
| { |
| "id": str(key), |
| "offsets": [ |
| [ |
| int(line_processed[7].split(":")[0]), |
| int(line_processed[7].split(":")[1]), |
| ] |
| ], |
| "text": [ |
| full_text[ |
| int( |
| line_processed[7].split(":")[0] |
| ) : int(line_processed[7].split(":")[1]) |
| ] |
| ], |
| "type": "", |
| "normalized": [], |
| } |
| ) |
| key += 1 |
| data["entities"].append( |
| { |
| "id": str(key), |
| "offsets": [ |
| [ |
| int(line_processed[8].split(":")[0]), |
| int(line_processed[8].split(":")[1]), |
| ] |
| ], |
| "text": [ |
| full_text[ |
| int( |
| line_processed[8].split(":")[0] |
| ) : int(line_processed[8].split(":")[1]) |
| ] |
| ], |
| "type": "", |
| "normalized": [], |
| } |
| ) |
| key += 1 |
| data["relations"].append( |
| { |
| "id": str(key), |
| "type": line_processed[-1].split("\n")[0], |
| "arg1_id": str(key - 2), |
| "arg2_id": str(key - 1), |
| "normalized": [], |
| } |
| ) |
| key += 1 |
| elif line_processed[2] == "concept": |
| data["entities"].append( |
| { |
| "id": str(key), |
| "offsets": [ |
| [ |
| int(line_processed[4]), |
| int(line_processed[5]), |
| ] |
| ], |
| "text": [ |
| full_text[ |
| int(line_processed[4]) : int( |
| line_processed[5] |
| ) |
| ] |
| ], |
| "type": line_processed[-1].split("\n")[0], |
| "normalized": [], |
| } |
| ) |
| key += 1 |
| yield key, data |
| key += 1 |
|
|
