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GENIA-Term-Corpus.py

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-import random
-import re
-import xml.etree.ElementTree as ET
-from typing import Tuple, List, Set
-from tqdm import tqdm
-
-import csv
-import json
-import os
-
-import datasets
-
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@InProceedings{huggingface:dataset,
-title = {A great new dataset},
-author={huggingface, Inc.
-},
-year={2020}
-}
-"""
-
-# TODO: Add description of the dataset here
-# You can copy an official description
-_DESCRIPTION = """\
-GENIA Term corpus
-"""
-
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = "http://www.geniaproject.org/genia-corpus/term-corpus"
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
-
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URLS = "http://www.nactem.ac.uk/GENIA/current/GENIA-corpus/Term/GENIAcorpus3.02.tgz"
-
-def _split_files(data_dir):
-    root = ET.parse(os.path.join(data_dir, "GENIA_term_3.02", "GENIAcorpus3.02.xml")).getroot()
-    articles = root.findall(".//article")
-
-    train_root = ET.Element("set")
-    dev_root = ET.Element("set")
-    test_root = ET.Element("set")
-
-    for a in articles:
-        root.remove(a)
-
-    random.shuffle(articles)
-
-    for a in articles[:1600]:
-        train_root.append(a)
-
-    for a in articles[1600:1800]:
-        dev_root.append(a)
-
-    for a in articles[1800:]:
-        test_root.append(a)
-
-    ET.ElementTree(train_root).write(os.path.join(data_dir, "train.xml"))
-    ET.ElementTree(dev_root).write(os.path.join(data_dir, "dev.xml"))
-    ET.ElementTree(test_root).write(os.path.join(data_dir, "test.xml"))
-
-class GENIATermCorpus(datasets.GeneratorBasedBuilder):
-
-    VERSION = datasets.Version("0.9.0")
-
-    pattern = re.compile(r"[,\.;:\[\]\(\)]")
-
-    def _info(self):
-
-        features = datasets.Features(
-            {
-                "tokens": datasets.Sequence(datasets.Value("string")),
-                "folded_tokens": datasets.Sequence(datasets.Value("string")),
-                "labels": datasets.Sequence(datasets.Value("string"))
-                    # datasets.features.ClassLabel(
-                    #     names=["O", ]
-                    # )
-                # )
-            }
-        )
-
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-
-        data_dir = dl_manager.download_and_extract(_URLS)
-        # Split the dataset files in train/dev/test
-        _split_files(data_dir)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "train.xml"),
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "test.xml"),
-                    "split": "test"
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "dev.xml"),
-                    "split": "dev",
-                },
-            ),
-        ]
-
-
-    def _generate_examples(self, filepath:str, split):
-        root = ET.parse(filepath)
-        articles = root.findall(".//article")
-        for idx, article in enumerate(articles):
-            article_id, data= self.parse_article(article)
-            for sen_ix, (tokens, entities) in enumerate(data):
-                yield f"{split}_{idx}_{sen_ix}", {
-                    "tokens": tokens,
-                    "folded_tokens": [t.lower() for t in tokens],
-                    "labels": entities
-                }
-
-    def parse_article(self, article:ET):
-        # Get the id of the article
-        article_id = article.find("./articleinfo/bibliomisc").text
-        # Select all sentences in the article object
-        sentences = article.findall(".//sentence")
-        data = list()
-        for sentence in sentences:
-            data.append(self. build_bio_tags(*self.flatten_tree(sentence)))
-
-        return article_id, data
-
-    def build_bio_tags(self, text_segments:List[str], entities:List[str]) -> Tuple[List[str], List[str]]:
-
-        # Hacky tokenizer
-        tokens, tags = list(), list()
-        for seg, entity in zip(text_segments, entities):
-            # Insert whitespaces
-            seg = self.pattern.sub(r" \g<0> ", seg).strip() # Remove trailing whitespaces
-            t = seg.split()
-            tokens.extend(t)
-            tags.extend( [f"B-{entity}"] + [f"I-{entity}"] * (len(t) - 1) if entity != "O" else ["O"] * len(t))
-        return tokens, tags
-
-
-    def flatten_tree(self, elem:ET) -> Tuple[List[str], List[str]]:
-        # Just keep the simple (not the nested) annotations
-        text_segments, entities = list(), list()
-        if  elem.text:
-            text_segments.append(elem.text)
-            if elem.tag == "cons" and "sem" in elem.attrib:
-                tag = elem.attrib['sem'].replace("G#", "")
-            else:
-                tag = "O"
-            entities.append(tag)
-        for child in elem:
-            c_segments, c_entities = self.flatten_tree(child)
-            text_segments.extend(c_segments)
-            entities.extend(c_entities)
-        if elem.tail and elem.tail != '\n':
-            text_segments.append(elem.tail)
-            entities.append("O")
-
-
-        return text_segments, entities

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default/genia-term-corpus-train.parquet

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default/genia-term-corpus-validation.parquet

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