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SemEval2018_Task7.py

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-# 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
-#
-# Unless required by applicable law or agreed to in writing, software
-# 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
-            }

Subtask_1_1/sem_eval2018_task7-test.parquet

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Subtask_1_1/sem_eval2018_task7-train.parquet

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