SemEval2018Task7.py

# I am trying to understand to the following code. Do not use this for any purpose as I do not support this.
# Use the original source from https://huggingface.co/datasets/DFKI-SLT/science_ie/raw/main/science_ie.py


# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Semeval2018Task7 is a dataset that describes the first task on semantic relation extraction and classification in scientific paper abstracts"""  



import glob
import datasets
import xml.dom.minidom
import xml.etree.ElementTree as ET

# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@inproceedings{gabor-etal-2018-semeval,
    title = "{S}em{E}val-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers",
    author = {G{\'a}bor, Kata  and
      Buscaldi, Davide  and
      Schumann, Anne-Kathrin  and
      QasemiZadeh, Behrang  and
      Zargayouna, Ha{\"\i}fa  and
      Charnois, Thierry},
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S18-1111",
    doi = "10.18653/v1/S18-1111",
    pages = "679--688",
    abstract = "This paper describes the first task on semantic relation extraction and classification in 
    scientific paper abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations 
    and includes three different subtasks. The subtasks were designed so as to compare and quantify the 
    effect of different pre-processing steps on the relation classification results. We expect the task to 
    be relevant for a broad range of researchers working on extracting specialized knowledge from domain 
    corpora, for example but not limited to scientific or bio-medical information extraction. The task 
    attracted a total of 32 participants, with 158 submissions across different scenarios.",
}
"""

# You can copy an official description
_DESCRIPTION = """\
This paper describes the first task on semantic relation extraction and classification in scientific paper
abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three 
different subtasks. The subtasks were designed so as to compare and quantify the effect of different
pre-processing steps on the relation classification results. We expect the task to be relevant for a broad 
range of researchers working on extracting specialized knowledge from domain corpora, for example but not 
limited to scientific or bio-medical information extraction. The task attracted a total of 32 participants, 
with 158 submissions across different scenarios.
"""

# Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://github.com/gkata/SemEval2018Task7/tree/testing"

# Add the licence for the dataset here if you can find it
_LICENSE = ""

# 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 = {
    "Subtask_1_1": {
        "train": {
            "relations": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.1.relations.txt",
            "text": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.1.text.xml",
        },
        "test": {
            "relations": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.1.test.relations.txt",
            "text": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.1.test.text.xml",
        },
    },
    "Subtask_1_2": {
        "train": {
            "relations": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.2.relations.txt",
            "text": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.2.text.xml",
        },
        "test": {
            "relations": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.2.test.relations.txt",
            "text": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.2.test.text.xml",
        },
    },
    
}


def all_text_nodes(root):
    if root.text is not None:
        yield root.text
    for child in root:
        if child.tail is not None:
            yield child.tail


def reading_entity_data(ET_data_to_convert):
  parsed_data = ET.tostring(ET_data_to_convert,"utf-8")
  parsed_data= parsed_data.decode('utf8').replace("b\'","")
  parsed_data= parsed_data.replace("<abstract>","")
  parsed_data= parsed_data.replace("</abstract>","")
  parsed_data= parsed_data.replace("<title>","")
  parsed_data= parsed_data.replace("</title>","")
  parsed_data = parsed_data.replace("\n\n\n","")

  parsing_tag = False
  final_string = ""
  tag_string= ""
  current_tag_id = ""
  current_tag_starting_pos = 0
  current_tag_ending_pos= 0
  entity_mapping_list=[]
  
  for i in parsed_data:
    if i=='<':
      parsing_tag = True
      if current_tag_id!="":
        current_tag_ending_pos = len(final_string)-1
        entity_mapping_list.append({"id":current_tag_id,
                                    "char_start":current_tag_starting_pos,
                                    "char_end":current_tag_ending_pos+1})
        current_tag_id= ""
        tag_string=""


    elif i=='>':
      parsing_tag = False
      tag_string_split = tag_string.split('"')
      if len(tag_string_split)>1:
        current_tag_id= tag_string.split('"')[1]
        current_tag_starting_pos = len(final_string)

    else:
      if parsing_tag!=True:
        final_string = final_string + i
      else:
        tag_string = tag_string + i

  return {"text_data":final_string, "entities":entity_mapping_list}



class Semeval2018Task7(datasets.GeneratorBasedBuilder):
    """
    Semeval2018Task7 is a dataset for semantic relation extraction and classification in scientific paper abstracts
    """

    VERSION = datasets.Version("1.1.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="Subtask_1_1", version=VERSION,
                               description="Relation classification on clean data"),
        datasets.BuilderConfig(name="Subtask_1_2", version=VERSION,
                               description="Relation classification on noisy data"),
        
    ]
    DEFAULT_CONFIG_NAME = "Subtask_1_1"

    def _info(self):
        class_labels = ["","USAGE", "RESULT", "MODEL-FEATURE", "PART_WHOLE", "TOPIC", "COMPARE"]
        features = datasets.Features(
            {
                "id": datasets.Value("string"),
                "title": datasets.Value("string"),
                "abstract": datasets.Value("string"),
                "entities": [
                    {
                        "id": datasets.Value("string"),
                        "char_start": datasets.Value("int32"),
                        "char_end": datasets.Value("int32")
                    }
                ],
                "relation": [
                    {
                        "label": datasets.ClassLabel(names=class_labels),
                        "arg1": datasets.Value("string"),
                        "arg2": datasets.Value("string"),
                        "reverse": datasets.Value("bool")
                    }
                ]
            }
        )

        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):
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        urls = _URLS[self.config.name]
        downloaded_files = dl_manager.download(urls)
        print(downloaded_files)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "relation_filepath": downloaded_files['train']["relations"],
                    "text_filepath": downloaded_files['train']["text"],
                    
                }

            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "relation_filepath": downloaded_files['test']["relations"],
                    "text_filepath": downloaded_files['test']["text"],
                    
                }

            )]
        
    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, relation_filepath, text_filepath):
        
        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        with open(relation_filepath, encoding="utf-8") as f:
            relations = []
            text_id_to_relations_map= {}
            for key, row in enumerate(f):
                row_split = row.strip("\n").split("(")
                use_case = row_split[0]
                second_half = row_split[1].strip(")")
                second_half_splits = second_half.split(",")
                size = len(second_half_splits)
                
                relation = {
                    "label": use_case,
                    "arg1": second_half_splits[0],
                    "arg2": second_half_splits[1],
                    "reverse": True if size == 3 else False
                }
                relations.append(relation)
                
                arg_id = second_half_splits[0].split(".")[0]
                if arg_id not in text_id_to_relations_map:
                  text_id_to_relations_map[arg_id] = [relation]
                else:
                  text_id_to_relations_map[arg_id].append(relation)
                  #print("result", text_id_to_relations_map)

                #for arg_id, values in text_id_to_relations_map.items():
                  #print(f"ID: {arg_id}")
                 # for value in values:
                  #  (value)

                

        doc2 = ET.parse(text_filepath)
        root = doc2.getroot()
        
        for child in root:
          if child.find("title")==None: 
            continue
          text_id = child.attrib
          #print("text_id", text_id)

          if child.find("abstract")==None: 
            continue
          title = child.find("title").text
          child_abstract = child.find("abstract")
          
          
          abstract_text_and_entities = reading_entity_data(child.find("abstract"))
          title_text_and_entities = reading_entity_data(child.find("title"))
          
          text_relations = []
          if text_id['id'] in text_id_to_relations_map:
            text_relations = text_id_to_relations_map[text_id['id']]

          yield text_id['id'], {
              "id": text_id['id'],
              "title": title_text_and_entities['text_data'],
              "abstract": abstract_text_and_entities['text_data'],
              "entities": abstract_text_and_entities['entities'] + title_text_and_entities['entities'],
              "relation": text_relations
            }