Initial commit with the dataset loader

GENIA-Term-Corpus.py

@@ -0,0 +1,192 @@
+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