Create SemEval2018_Task7.py

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
+            }